2 Evaluation of short term effect of PAH and other pollutants on natural and

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1 Authors

G. Cesaroni, M. Stafoggia, C. Badaloni, A. Faustini, C. Gariazzo, F. Forastiere

Epidemiology Dept. of Regional Health Service, Rome

Technical report on the short-term and long-term health effects of exposure to PAHs, BaP, and PM2.5. Health impact according to the base-case and the alternative mitigation scenarios.

Action 6.1, 6.2, 6.3 and 7.3

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2

1 Executive Summary 3

2 Evaluation of short term effect of PAH and other pollutants on natural and

cause specific mortality (Action 6.1) 5

2.1 Introduction 2.2 Methods 2.3 Results 2.4 Conclusions 2.5 References

3 Evaluation of short term effect of PAH and other pollutants on emergency

hospital admissions (Action 6.2 Report) 19

4 Long-term exposure to polycyclic aromatic hydrocarbons (PAH), mortality and incidence of lung cancer, acute coronary events and stroke in the Rome Longitudinal Study, 2008-2012 - the EXPAH (Population Exposure to PAHs)

project (Action 6.3 Report) 33

4.1 Introduction 4.2 Methods 4.3 Results 4.4 Discussion 4.5 Conclusions

4.6 References and Tables

5 Health impact assessment of long-term exposure to air pollutants for

selected scenarios (Action 7.3 Report) 48

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3

1 Executive Summary

There is convincing evidence from scientific literature on the short-term effects of particulate matter, especially fine particles (PM2.5), involving mortality and hospitalizations for cardiovascular and respiratory causes. In addition, long-term exposure to air pollution has been related to mortality, incidence of cardiovascular diseases and lung cancer. On October 2013, air pollution in general, and particulate matter in particular, have been classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC). Among the most carcinogenic compounds of particulate matter, polycyclic aromatic hydrocarbons (PAH), specifically benzo[a]pyrene (BaP), have been identified. However, the possible role of such PM compounds in triggering acute outcomes and causing morbidity and premature mortality from cardiorespiratory causes is still unknown.

The objectives of the EXPAH project were to estimate population exposure to PM2.5, total PAHs, and BaP, and to investigate their short-term and long-term association with morbidity and mortality in Rome, Italy. In addition, the project aimed to provide, following a Health Impact Assessment approach, the estimate of the number of premature deaths for non-accidental causes attributable to long-term PM2.5 exposure in the resident population.

Different study populations were specifically selected for each outcome. To study the short-term effects of air pollution on mortality, we selected all subjects deceased in Rome between June 2011 and May 2012 and residing in the city at the time of death. To evaluate short-term effects of air pollution on morbidity we selected all subjects admitted to hospital for acute and non-scheduled hospitalizations in Rome between June 2011 and May 2012 and residing in the city at the time of admission. Finally, to study the long-term effects of air pollution exposure on mortality, incidence of acute coronary event, stroke, and lung cancer we used the Rome Longitudinal Study, a cohort study based on the 2001 census an including about two million subjects, selecting subjects aged 40 years or more at Jan 2008 and following them till Dec 2012.

For short term effects, individual exposures to PAHs, BaP and PM2.5 were assigned at residential addresses using the results of the dispersion models with a 1 km spatial resolution (EXPAH action 4.5). In addition, as alternative PM2.5 exposure, we considered daily average PM2.5 concentrations

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4 measured in Rome by the Lazio Region Environmental Protection Agency (ARPA-Lazio) monitoring network. As alternative PAH and BaP exposure definition, we considered the concentrations modeled with monthly adjustment of the predictions (as better explained in reports of actions 4.5). For long-term outcomes, individual annual mean exposure was assigned through the coordinates of residential addresses at inclusion using dispersion models results of pollutants with a 1 km spatial resolution. For the health impact assessment we estimated the annual average exposure to air pollution of the 2011 Census population of Rome at baseline and at different scenarios.

We found evidence of an association of all the exposures with short-term natural and respiratory mortality, both at delayed and prolonged latencies, whereas the effects on cardiovascular mortality were somewhat weaker. Elderly and subjects dying on colder months were vulnerable to the effects of PAH, while there were no differences according to gender and place of death (at home versus in-hospital). Conversely, we found little to no evidence of an association of any of the exposures with cardio-respiratory emergency hospitalizations.

We found evidence of an association between long-term exposure to all considered pollutants and non-accidental and cardiovascular mortality. We estimated a 4% increased risk of non-accidental mortality and a 7% increased risk of cardiovascular mortality per increases of 5 µg/m³ in annual mean PM2.5 (HR=1.04, 95%CI: 1.01-1.07 and HR=1.07, 95%CI: 1.03-1.12, respectively). We found strong evidence of association between long-term exposure to PAHs, BaP, and PM2.5 and incidence of stroke and lung cancer.

We found a high impact of long-term exposure to PM2.5 on non-accidental mortality: 1,184 deaths among men and 1,351 deaths among women were attributable to PM2.5 exposure each year, using the base case model. Based on the model results available for two 2020 emission scenarios (described in Actions 7.1 and 7.2), we found that for the scenario at 2020 with current legislation plus additional measures, the number of non-accidental deaths attributable to PM2.5 exposure would be much lower than those in the base case model, i.e. 676 deaths in men and 749 deaths in women.

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5

2 Evaluation of short term effect of PAH and other pollutants on natural and cause specific mortality (Action 6.1 Report)

2.1 Introduction

There is convincing evidence from scientific literature on the short-term effects of particulate matter, especially fine particles (PM2.5), on mortality for cardiovascular and respiratory causes.^1,2 On October 2013, air pollution in general, and particulate matter in particular, have been classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC).⁸ Among the most carcinogenic compounds of particulate matter, polycyclic aromatic hydrocarbons (PAH), specifically benzo[a]pyrene (BaP), have been identified and investigated. However, the possible role of such PM compounds in triggering acute outcomes, especially mortality from cardiorespiratory causes, is still unknown.

The objective of the EXPAH project was to estimate population exposure to PM2.5, total PAHs, and BaP, and to investigate their long-term and short-term association with mortality and morbidity in Rome, Italy. The present report displays the results on the association between short-term exposure to PM2.5, total PAHs, and BaP, and cause-specific mortality.

2.2 Methods

Study population and outcome definition

In this study we selected all subjects deceased in Rome between June 2011 and May 2012 and residing in the city at the time of death. We retrieved information on age, gender, season of death (classified as “cold season”, October to March, and “warm season”, April to September), place of death (home, hospital, other or missing), and underlying cause. The underlying cause of death (coded according to the International Classification of Diseases revision 9, ICD-9) for deceased subjects was obtained from the regional health information system. We considered 4 main groups of death causes: natural (ICD-9: 1-799), cardiac (ICD-9: 390-429), cerebrovascular (ICD-9: 430-438), and respiratory causes (ICD-9: 460-519).

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6 Exposure assessment

Individual exposure to PAHs, BaP and PM2.5 was assigned at residential address using dispersion models with a 1 km spatial resolution. Daily PAHs concentrations have been estimated by model including 4 PAHs congeners: Benzo[a]Pyrene(B[a]P), Benzo[b]fluoranthene (B[b]f), Benzo[k]fluoranthene (B[k]f) and Indeno Pyrene (IP). They are considered in the list of most carcinogenic PAHs compounds (eg. Agency for Toxic Substances and Disease Registry (ATSDR)).

The sum of these congeners will be identified as PAHs hereafter. Details on development of dispersion models and results are presented in reports of actions 4 (EXPAH, 2013). These exposures will be referred to as “case-base” in the report. In addition, as alternative PM2.5

exposure, we considered daily average PM2.5 concentrations over Rome, as measured by the Lazio Region Environmental Protection Agency (ARPA-Lazio) monitoring network (“ARPA” PM2.5

throughout the text). Finally, as alternative PAH and BaP exposures we considered the corrected modeled concentrations, as better explained in reports of actions 4 (“corrected” PAH and BaP throughout the text) (EXPAH, 2013), derived by the application to the case-base modeled results of observed/modeled PAHs monthly correction factors to overcome overestimations of observed PAHs concentrations. These exposures will be referred to as “assimilated” in the report

Additional variables

Other time-varying variables were collected in order to adjust for potential confounding in the epidemiological analysis. They include: air temperature; holidays (a two-level variable assuming value “1” on Christmas and Easter, their surrounding periods, and isolated holidays; “0” on other days); summer population decrease (a three-level variable assuming value “2” in the 2-week period around the 15th of August; value “1” from the 16th of July to 31st of August, with the exception of the aforementioned period; value “0” all other days); influenza epidemics (a two- level variable assuming value “0” on normal days, value “1” on days with particularly high influenza episodes, as identified using the Italian influenza surveillance system).

Statistical analyses

The analyses were carried out using a case-crossover approach with “time-stratified” design for the selection of control days. More specifically, for each deceased subject (case), control days were defined as the same days of the week within the same month and year of death. In this way,

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7 long-term as well as seasonal time trends and day-of-the-week effects were controlled for. The statistical analysis was performed with multivariate conditional logistic regression models, also adjusting for air temperature, holidays, summer population decrease, and influenza epidemics.

The effect of temperature was controlled for by modeling high and low temperatures separately.

High temperatures were adjusted for by calculating the average of current and previous day temperature (lag 0-1) and by fitting a penalized spline on the lagged variable only for days with lag 0-1 temperature above the median value. Similarly, low temperatures were adjusted for by fitting a penalized spline on lag 1-6 air temperature only for days below the median value. The other confounders, including holidays, summer population decrease, and influenza epidemics, were adjusted for with indicator variables.

Once the adjustment model was defined, the pollutant was added to the regression model. The lag structure of the association between PM2.5/PAH/BaP concentrations and cause-specific mortality was inspected by choosing three cumulative lag structures a priori defined to represent immediate, delayed or prolonged effects: lags 0-1, lags 2-5 and lags 0-5, respectively. Finally, for each exposure/outcome combination, one of these three alternatives was chosen as the

“reference lag” for the following analyses.

Effect modification by age, gender, season of death, and place of death, was evaluated by adding interaction terms between each of these effect modifier, in turn, and the exposure of analysis.

Statistical tests were performed to evaluate whether effect estimates were different across strata of each effect modifier. Finally, concentration-response shapes were explored by fitting the exposure variable as a natural spline with 3 knots equally spaced over the range of exposure concentrations.

All the results are expressed as percent increases of mortality, and 95% CI, relative to increments in each exposure equal to its interquartile range, calculated on the distribution of differences between case-day exposure and control-days exposures: 1.61 ng/m³ and 0.65 ng/m³ for case-base and assimilated PAH, respectively; 0.47 ng/m³ and 0.17 ng/m³ for case-base and assimilated BaP, respectively; 13.08 μg/m³ and 14.00 μg/m³ for case-base and ARPA PM2.5, respectively. These increments have been chosen in order to represent the same amount of daily variability across pollutants, so to allow a better inter-pollutant comparison of results.

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8 2.3 Results

Tables 1a and 1b show the distributions of exposures to PAHs, BaP, and PM2.5 across the studied population. Table 1a reports the exposures at residential address, while Table 1b reports the distributions of the differences in exposures between case-days and control-days. The IQRs of the latter are used as metrics for the epidemiological evaluations.

Table 1a. Descriptive statistics of environmental variables over Rome - 01/06/2011 to 31/05/2012.

Values based on the residences of subjects deceased from natural causes (N.= 22,172) PAH and BaP concentrations are expressed as ng/m³; PM2.5 concentrations as µg/m³

Variables Mean SD Percentiles

min 5^th 25^th 50^th 75^th 95^th max IQR²

PAH (ng/m³)

Case-base ^5.47 ^6.23 0.02 0.25 0.48 3.13 8.57 18.06 37.50 8.08

Assimilated ^2.38 ^2.56 0.01 0.18 0.33 1.31 3.87 7.67 14.70 3.55

BaP (ng/m³)

Case-base ^1.62 ^1.90 0.00 0.05 0.11 0.90 2.59 5.47 11.28 2.48

Assimilated ^0.59 ^0.64 0.00 0.03 0.07 0.33 0.96 1.91 3.94 0.89

PM2.5 (µg/m³)

Case-base ^20.65 ^11.29 1.49 7.28 12.38 17.85 26.64 43.57 68.24 14.26

ARPA stations¹ ^20.32 ^10.77 3.00 7.00 12.50 18.00 26.00 43.00 60.00 13.50

Air temperature ^15.50 ^7.30 -1.00 3.10 10.27 14.90 22.15 26.40 29.95 11.88

1 Computed as daily average over entire Rome (no spatial variability)

2 InrerQuartile Range, computed as the difference between the 75th and the 25th percentile

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9 Table 1b. Descriptive statistics of environmental variables over Rome - 01/06/2011 to 31/05/2012.

Values based on the residences of subjects deceased from natural causes (N.= 22,172), and calculated as the difference between case-day exposure and control-days exposures.

PAH and BaP concentrations are expressed as ng/m³; PM2.5 concentrations as µg/m³

PAH (ng/m³)

Case-base -0.01 4.24 -26.54 -7.30 -0.81 0.00 0.81 7.13 25.93 1.61

Assimilated -0.01 1.86 -11.25 -3.29 -0.33 0.00 0.32 3.19 11.88 0.65

BaP (ng/m³)

Case-base 0.00 1.28 -7.96 -2.20 -0.23 0.00 0.23 2.15 7.78 0.47

Assimilated 0.00 0.47 -3.03 -0.83 -0.08 0.00 0.08 0.80 3.19 0.17

PM2.5 (µg/m³)

Case-base 0.08 12.55 -58.56 -20.60 -6.41 0.11 6.67 20.71 57.79 13.08

ARPA stations¹ 0.13 12.82 -52.00 -21.00 -7.00 0.00 7.00 21.00 52.00 14.00

Air temperature 0.03 4.12 -11.65 -6.90 -2.75 0.00 2.82 6.88 11.65 5.57

Correlation coefficients between pairs of exposures are reported in Table 2. High correlations (>

0.90) are detected between different estimates of the same pollutant, and between total PAH and BaP. Correlations between PAH (BaP) and PM2.5 are lower but still very high, in the range of 0.60- 0.70. For this reason, no two-pollutant analysis (analyses with two pollutants in the same regression model) were performed.

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10 Table 2. Pearson correlations between daily averages of environmental variables: 01/06/2011 to 31/05/2012. Values based on the residences of subjects deceased from natural causes (N.=

22,172)

Variables PAH

c-b

PAH

assimil. BaP c-b

BaP

assimil. PM2.5

c-b

PM2.5

ARPA Temp.

Airport

PAH Case-base ^1.00

PAH Assimilated ^0.95 ^1.00

BaP Case-base ^1.00 ^0.95 ^1.00

BaP Assimilated ^0.92 ^1.00 ^0.92 ^1.00

PM2.5 Case-base ^0.74 ^0.75 ^0.74 ^0.74 ^1.00

PM2.5 ARPA stations ^0.64 ^0.64 ^0.64 ^0.63 ^0.95 ^1.00

Temperature Airport monitor ^-0.70 ^-0.71 ^-0.71 ^-0.72 ^-0.48 ^-0.37 ^1.00

Table 3 reports descriptive statistics of the deceased subjects. There were a total of 22,172 deaths from natural causes, 37% of which were for cardiovascular causes, and 7% for respiratory causes.

Most of the deceased subjects were in the age classes 75-84 years (33%) and 85+ years (40%).

Most of the people died in the colder months (55%), and in hospital (61%).

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11 Table 3. Population deceased in Rome and resident in the city - 01/06/2011 to 31/05/2012

Variables No. %

Total population, deceased from natural causes ^22,172 ^100.0 Causes of death (ICD-9 code)

Cardiovasculardisease (ICD: 390-459) ^8,204 ^37.0

Cardiacdisease (ICD: 390-429) ^6,265 ^28.3

Cerebrovasculardisease (ICD: 430-438) ^1,628 ^7.3

Respiratorydiseases (ICD: 460-519) ^1,508 ^6.8

Age (years)

0 - 34 164 0.7

35-64 2,445 11.0

65-74 3,525 15.9

75-84 7,256 32.7

85+ 8,782 39.6

Gender

Men 10,489 47.3

Women 11,683 52.7

Season of death

Cold (October-March) 12,104 54.6

Warm (April-September) 10,068 45.4

Place of death

Home 7,556 34.1

In hospital 13,468 60.7

Other or missing 1,148 5.2

Tables 4a, b and c display the main results of the study, concerning the associations between short-term exposures to PAH (Table 4a), BaP (Table 4b), and PM2.5 (Table 4c), with cause-specific mortality, by lag. Strong and statistically significant associations are found for PAH and BaP with natural mortality, especially at delayed (2-5) and prolonged (0-5) lags, with associations very similar between PAH and BaP. Higher associations, but non-significant due to lack of power, are detected for the two exposures with respiratory mortality. No clear association is found with cardiovascular mortality. There is no difference in effect estimates between the two methods used for exposure assessment (Tables 4a and 4b).

Non-significant associations between PM2.5 and mortality are found, with some evidence of an effect on cerebrovascular and respiratory mortality, and no associations with cardiac mortality.

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12 Table 4a. Effect of PAH on mortality, by cause, lag and model used for exposure assessment. Results expressed as % increases of risk (% IR) for IQR increases of the pollutant, calculated on the difference between cases and controls. PAH concentrations are expressed as ng/m³

Variables Natural Cardiac Cerebrovascular Respiratory

% IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI

PAH, case-base (IQR=1.61)

lag 0-1 0.12 -1.25 1.52 -1.21 -3.68 1.32 -1.24 -6.03 3.80 4.06 -1.52 9.95

lag 2-5 2.39 0.45 4.37 1.82 -1.51 5.27 -0.24 -6.88 6.88 2.99 -4.39 10.94

lag 0-5 2.39 ^0.06 ^4.77 0.76 ^-3.29 ^4.99 -1.34 ^-9.12 ^7.11 6.50 ^-2.60 ^16.44 PAH, assimilated (IQR=0.65)

lag 0-1 -0.02 ^-1.23 ^1.21 -1.08 ^-3.25 ^1.15 -1.10 ^-5.38 ^3.37 3.12 ^-1.67 ^8.14 lag 2-5 2.03 ^0.47 ^3.60 1.58 ^-1.09 ^4.32 -0.45 ^-5.77 ^5.18 2.76 ^-3.22 ^9.10 lag 0-5 1.98 ^0.10 ^3.90 0.91 ^-2.37 ^4.31 -1.26 ^-7.58 ^5.48 5.30 ^-2.11 ^13.28 In bold, results statistically significant at 95% level (95% CI not overlapping 0)

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13 Table 4b. Effect of BaP on mortality, by cause, lag and model used for exposure assessment. Results expressed as % increases of risk (% IR) for IQR increases of the pollutant, calculated on the difference between cases and controls. BaP concentrations are expressed as ng/m³

BaP, case-base (IQR=0.47)

lag 0-1 0.07 -1.26 1.42 -1.20 -3.59 1.24 -1.38 -6.02 3.48 3.85 -1.54 9.53

lag 2-5 2.38 0.50 4.29 1.88 -1.35 5.22 -0.08 -6.52 6.81 2.80 -4.36 10.51

lag 0-5 2.36 0.09 4.68 0.85 -3.11 4.97 -1.34 -8.94 6.89 6.23 -2.65 15.91

BaP, assimilated (IQR=0.17)

lag 0-1 -0.09 -1.33 1.15 -1.10 -3.31 1.16 -1.27 -5.62 3.27 2.88 -1.93 7.93

lag 2-5 1.91 0.36 3.48 1.69 -1.00 4.46 -0.53 -5.81 5.06 2.65 -3.31 8.97

lag 0-5 1.83 -0.06 3.75 0.96 -2.32 4.36 -1.45 -7.76 5.29 5.00 -2.41 12.97 In bold, results statistically significant at 95% level (95% CI not overlapping 0)

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14 Table 4c. Effect of PM2.5 on mortality, by cause, lag and model/source used for exposure assessment. Results expressed as % increases of risk (%

IR) for IQR increases of the pollutant, calculated on the difference between cases and controls. PM2.5 concentrations are expressed as ug/m³

PM2.5, case-base (IQR=13.08)

lag 0-1 0.92 -2.60 4.56 -1.89 -8.04 4.67 -1.12 -13.18 12.63 13.29 -1.55 30.37

lag 2-5 0.49 -3.33 4.46 -0.54 -7.12 6.51 4.96 -8.72 20.70 7.57 -8.10 25.91

lag 0-5 1.02 -3.60 5.87 -1.59 -9.50 7.00 4.45 -11.76 23.64 14.36 -5.25 38.02

PM2.5, ARPA monitors (IQR=14.00)

lag 0-1 1.75 -2.00 5.65 -0.77 -7.26 6.18 1.64 -11.72 17.01 15.29 -0.51 33.59

lag 2-5 0.47 -3.53 4.65 -0.10 -7.33 7.70 6.14 -8.42 23.01 6.64 -9.53 25.70

lag 0-5 1.52 -3.41 6.71 -0.84 -9.49 8.63 7.04 -10.78 28.41 14.98 -5.79 40.33 In bold, results statistically significant at 95% level (95% CI not overlapping 0)

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15 On the basis of previous results, lag 2-5 has been chosen as the reference lag for the association between PAH and cause-specific analysis. Since PAH and BaP displayed identical results, and PM2.5

had no statistically significant association with cause-specific mortality, further analyses on effect modification and concentration-response were carried on for PAH only.

Table 5 report the results of effect modification analysis, showing highest effects of PAH (case- base, lag 2-5) on natural mortality in the elderly and in subjects died during the colder months, with no apparent difference according to gender or place of death.

Table 5. Effects of PAH (lag 2-5, case-base) on natural mortality, by individual characteristics.

Results expressed as % increases of risk (% IR) for IQR=1.61 ng/m³ increases of the pollutant

Variables

Effects per 1.61 ng/m³

% IR 95% CI P int

Overall 2.39 0.45 4.37 -

Age

0-64 3.39 -0.93 7.89 -

65-84 1.16 -1.24 3.62 0.345

85+ 3.55 0.98 6.19 0.946

Gender

Males 2.14 -0.28 4.63 -

Females 2.62 0.27 5.02 0.741

Season of death

Cold (October-March) 3.04 1.05 5.07 -

Warm (April-September) -16.50 -26.48 -5.16 0.001

Place of death

Home 2.17 -0.53 4.93 -

In hospital 2.71 0.46 5.00 0.722

Other or missing -0.04 -6.58 6.96 0.542 In bold, results statistically significant at 95% level (95% CI not overlapping 0)

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16 Figure 1 displays the concentration-response relationships between cause-specific mortality and PAH (case-base, lag 2-5) exposure. A linear relationship with natural and respiratory mortality is apparent, whereas no clear trend of association emerges with cardiac and cerebrovascular causes of death.

Figure 1. Concentration-response curves of PAH (lag 2-5, case-base) and cause-specific mortality.

PAH concentrations are expressed as ng/m³

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17 2.4 Conclusions

We found evidence of an association of all the exposures with natural and respiratory mortality, especially at delayed and prolonged latencies, whereas the effects on cardiovascular mortality were somewhat weaker. Despite the short time-series, and the limited power for the statistical analyses, the associations between PAH/BaP and natural mortality were statistically significant, with a clear evidence of a linear relationship, and no evidence of lack of effect at low concentrations. Elderly and subjects dying on colder months were vulnerable to the effects of PAH, while there were no differences according to gender and place of death.

This is one of the few studies investigating the short-term association between PAH and mortality.

More studies are therefore needed to replicate these findings, possibly in other settings and using longer time-series.

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18 2.5 References

1. Brook RD, Rajagopalan S, Pope CA 3rd, et al. American Heart Association Council on

Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010; 121:2331-78.

2. Ruckerl R, Schneider A, Breitner S, et al. Health effects of particulate air pollution: a review of epidemiological evidence. Inhalation Toxicology, 2011; 23: 555-592.

3. Beelen R, Raaschou-Nielsen O, Stafoggia M, et al. Effects of long-term exposure to air pollution on natural cause mortality: an analysis of 22 European cohorts within the multi-center ESCAPE project. Lancet, 2014; 383: 785-795.

4. Cesaroni G, Badaloni C, Gariazzo C, et al. Long-term exposure to urban air pollution and mortality in a cohort of more than a million adults in Rome. Environ Health Perspect.

2013;121:324-31.

5. Cesaroni G, Forastiere F, Stafoggia M, et al. Long-term exposure to ambient air pollution and incidence of acute coronary events -Analysis of eleven European cohorts from the ESCAPE Project. BMJ. 2014 Jan 21;348:f7412. doi: 10.1136/bmj.f7412.

6. EXPAH - Technical report on FARM model capability to simulate PM2.5 and PAHs in the base case – Action 4.5, 2013 (http://www.ispesl.it/expah/documenti/R2013-

06_ARIANET_EXPAH_A4.5_final.pdf )

7. Stafoggia M, Cesaroni G, Peters A, et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events – Results from eleven European cohorts within the ESCAPE Project. Environ Health Perspect. 2014 May 16. [Epub ahead of print]

8. Raaschou-Nielsen O, Andersen ZJ, Beelen R, et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). Lancet Oncol. 2013;14:813-22. doi: 10.1016/S1470-2045(13)70279-1.

9. IARC: Outdoor air pollution a leading environmental cause of cancer deaths. Press Release 221.

17 October 2013.(available at http://www.iarc.fr/en/media-centre/pr/2013/pdfs/pr221_E.pdf)

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3 Evaluation of short term effect of PAH and other pollutants on emergency hospital admissions

(Action 6.2 Report)

3.1 Introduction

There is convincing evidence from scientific literature on the short-term effects of particulate matter, especially fine particles (PM2.5), on mortality and hospitalizations for cardiovascular and respiratory causes.^1,2 Also long-term exposure to air pollution has been related to mortality,^3,4 incidence of cardiovascular diseases,^5,6 and cancer incidence.⁷ On October 2013, air pollution in general, and specifically particles pollution, have been classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC).⁸ Among the most carcinogenic compounds of particulate matter, polycyclic aromatic hydrocarbons (PAH), specifically benzo[a]pyrene (BaP), have been identified and investigated. However, the possible role of such PM compounds in triggering acute outcomes, especially mortality or hospital admissions from cardiorespiratory causes, is still unknown.

The objective of the EXPAH project was to estimate population exposure to PM2.5, total PAHs, and BaP, and to investigate their long-term and short-term association with mortality and morbidity in Rome, Italy.

The present report displays the results on the association between short-term exposure to PM2.5, total PAHs, and BaP, and disease-specific emergency hospital admissions.

3.2 Methods

Study population and outcome definition

In this study we selected all subjects hospitalized in Rome between June 2011 and May 2012 and residing in the city at the moment of admission. Only ordinary, acute and non-scheduled admissions were considered. For each hospitalized subject, we retrieved information on age,

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20 gender, season of hospitalization (classified as “cold season”, October to March, and “warm season”, April to September), and primary diagnosis at discharge. The diagnoses (coded according to the International Classification of Diseases revision 9, ICD-9) were obtained from the regional health information system. We considered 7 main groups of diagnoses: cardiovascular diseases (ICD-9: 390-459), ischemic heart diseases (ICD-9: 410-414), heart failure (ICD-9: 428), cerebrovascular diseases (ICD-9: 430-438), respiratory diseases (ICD-9: 460-519), chronic obstructive pulmonary disease (ICD-9: 490-492, 494, 496), and asthma (ICD-9: 493).

Exposure assessment

Individual exposure to PAHs, BaP and PM2.5 was assigned at residential address using the same methods described in section 2.2.

Additional variables

Other time-varying variables were collected for adjusting for potential confounding in the epidemiological analysis. They include: air temperature; holidays (a four-level variable assuming value “3” on Christmas and Easter; “2” in the surrounding periods of Christmas and Easter; “1” on isolated holidays; “0” on other days); summer population decrease (a three-level variable assuming value “2” in the 2-week period around the 15th of August; value “1” from the 16th of July to 31st of August, with the exception of the aforementioned period; value “0” all other days);

influenza epidemics (a two-level variable assuming value “0” on normal days, value “1” on days with particularly high influenza episodes, as identified using the Italian influenza surveillance system).

Statistical analyses

The analyses were carried out using a case-crossover approach with “time-stratified” design for the selection of control days. More specifically, for each hospital admission day (case), control days were defined as the same days of the week within the same month and year of death. In this way, long-term as well as seasonal time trends and day-of-the-week effects were controlled for.

The statistical analysis was performed with multivariate conditional logistic regression models, also adjusting for air temperature, holidays, summer population decrease, and influenza epidemics.

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21 The effect of temperature was controlled for by modeling high and low temperatures separately.

High temperatures were adjusted for by calculating the average of current and previous day temperature (lag 0-1) and by fitting a penalized spline on the lagged variable only for days with lag 0-1 temperature above the median value. Similarly, low temperatures were adjusted for by fitting a penalized spline on lag 1-6 air temperature only for days below the median value. The other confounders, including holidays, summer population decrease and influenza epidemics, were adjusted for with indicator variables.

Once the adjustment model was defined, the pollutant was added to the regression model. The lag structure of the association between PM2.5/PAH/BaP concentrations and disease-specific hospitalization was inspected by choosing three cumulative lag structures a priori defined to represent immediate, delayed or prolonged effects: lags 0-1, lags 2-5 and lags 0-5, respectively.

Finally, for each exposure/outcome combination, one of these three alternatives was chosen as the “reference lag” for the following analyses.

Effect modification by age, gender and season was evaluated by adding interaction terms between each of these effect modifier, in turn, and the exposure of analysis. Statistical tests were performed to evaluate whether effect estimates were different across strata of each effect modifier. Finally, concentration-response shapes were explored by fitting the exposure variable as a natural spline with 3 knots equally spaced over the range of exposure concentrations.

All the results are expressed as percent increases of mortality, and 95% CI, relative to increments in each exposure equal to its interquartile range, calculated on the distribution of differences between case-day exposure and control-days exposures: 1.56 ng/m³ and 0.64 ng/m³ for case-base and assimilated PAH, respectively; 0.45 ng/m³ and 0.16 ng/m³ for case-base and assimilated BaP, respectively; 13.23 μg/m³ and 14.00 μg/m³ for case-base and ARPA PM2.5, respectively. These increments have been chosen in order to represent the same amount of daily variability across pollutants, so to allow a better inter-pollutant comparison of results.

3.3 Results

Tables 1a and 1b show the distributions of exposures to PAHs, BaP, and PM2.5 across the studied population. Table 1a reports the exposures at residential address, while Table 1b reports the

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22 distributions of the differences in exposures between case-days and control-days. The IQRs of the latter are used as metrics for the epidemiological evaluations.

Table 1a. Descriptive statistics of environmental variables over Rome - 01/06/2011 to 31/05/2012.

Values based on the residences of subjects hospitalized for cardiorespiratory diseases (N.= 39,682) PAH and BaP concentrations are expressed as ng/m³; PM2.5 concentrations as µg/m³

Variables Mean SD

Percentiles

PAH (ng/m³)

Case-base 5.35 6.14 0.02 0.25 0.50 3.08 8.26 17.89 37.50 7.76

Assimilated 2.32 2.51 0.01 0.17 0.33 1.27 3.69 7.57 13.08 3.35

BaP (ng/m³)

Case-base 1.59 1.87 0.00 0.05 0.11 0.88 2.49 5.40 11.28 2.38

Assimilated 0.57 0.63 0.00 0.03 0.07 0.32 0.91 1.90 3.52 0.84

PM2.5 (µg/m³)

Case-base ^20.52 ^11.36 1.49 7.18 12.26 17.58 26.29 43.49 69.56 14.03

ARPA stations¹ ^20.24 ^10.84 3.00 7.00 12.50 18.00 26.00 43.00 60.00 13.50

Air temperature ^15.45 ^7.13 -1.00 3.35 10.35 14.95 21.73 26.35 29.95 11.38

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23 Table 1b. Descriptive statistics of environmental variables over Rome - 01/06/2011 to 31/05/2012.

Values based on the residences of subjects hospitalized for cardiorespiratory diseases (N.=

39,682), and calculated as the difference between case-day exposure and control-days exposures.

PAH and BaP concentrations are expressed as ng/m³; PM2.5 concentrations as µg/m³

PAH (ng/m³)

Case-base 0.02 4.26 -26.25 -7.31 -0.76 0.00 0.80 7.40 26.54 1.56

Assimilated 0.01 1.85 -11.88 -3.26 -0.32 0.00 0.32 3.28 10.43 0.64

BaP (ng/m³)

Case-base 0.00 1.29 -7.88 -2.21 -0.22 0.00 0.23 2.23 7.96 0.45

Assimilated 0.00 0.47 -3.19 -0.82 -0.08 0.00 0.08 0.82 2.82 0.16

PM2.5 (µg/m³)

Case-base 0.09 12.68 -58.71 -20.63 -6.53 0.02 6.70 21.10 58.70 13.23

ARPA stations¹ 0.08 12.92 -52.00 -20.50 -7.00 0.00 7.00 21.50 52.00 14.00

Air temperature 0.00 4.08 -11.65 -6.75 -2.75 0.00 2.75 6.75 11.65 5.50

Correlation coefficients between pairs of exposures are reported in Table 2. High correlations (>

0.90) are detected between different estimates of the same pollutant, and between total PAH and BaP. Correlations between PAH (BaP) and PM2.5 are lower but still very high, in the range of 0.60- 0.70. For this reason, no two-pollutant analysis (analyses with two pollutants in the same regression model) were performed.

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24 Table 2. Pearson correlations between daily averages of environmental variables: 01/06/2011 to 31/05/2012. Values based on the residences of subjects hospitalized for cardiorespiratory diseases (N.= 39,682)

Variables PAH

c-b

PAH

assimil. BaP c-b

BaP

assimil. PM2.5

c-b

PM2.5

ARPA Temp.

Airport

PAH Case-base ^1.00

PAH Assimilated ^0.95 ^1.00

BaP Case-base ^1.00 ^0.95 ^1.00

BaP Assimilated ^0.93 ^1.00 ^0.92 ^1.00

PM2.5 Case-base ^0.74 ^0.74 ^0.74 ^0.74 ^1.00

PM2.5 ARPA stations ^0.64 ^0.64 ^0.64 ^0.63 ^0.95 ^1.00 Temperature Airport

monitor ^-0.69 ^-0.70 ^-0.70 ^-0.70 ^-0.46 ^-0.36 ^1.00

Table 3 reports descriptive statistics of the hospitalized subjects. There were a total of 39,682 emergency hospitalizations for cardio-respiratory diseases, 69% of which were for cardiovascular causes, and 31% for respiratory causes. Most of the hospitalized subjects were older than 65 years of age (53%), and men (54%). Most of the people were admitted to a hospital in the colder seasons (55%).

Tables 4a, b, and c display the main results of the study, concerning the associations between short-term exposures to PAH (Table 4a), BaP (Table 4b), and PM2.5 (Table 4c), with disease-specific hospitalizations, by lag. Hospital admissions for COPD or asthma were not included in this analysis as separate categories due to the very small counts of events, preventing the possibility of detecting any meaningful association. In general, no association was found between either PAH or BaP with cardio-respiratory hospital admissions, with positive but small and non-significant associations only at lag 0-1 for total cardiovascular and total respiratory admissions (Tables 4a and 4b). Non-significant associations between PM2.5 and hospitalizations were found, with some suggestion of an immediate effect on cardiovascular morbidity and a prolonged effect on respiratory morbidity (Table 4c).

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25 Table 3. Emergency hospital admissions for cardio-respiratory diseases occurred in Rome among subjects resident in the city - 01/06/2011 to 31/05/2012

Variables No. %

Total number of hospitalizations for cardio-respiratory disease ^39,682 ^100.0 Primarydiagnosis (ICD-9 code)

Cardiovasculardisease (ICD: 390-459) ^27,287 ^68.8

Ischemicheartdisease (ICD: 410-414) ^6,774 ^17.1

Heartfailure (ICD: 428) ^5,235 ^13.2

Cerebrovasculardisease (ICD: 430-438) ^6,430 ^16.2

Respiratorydiseases (ICD: 460-519) ^12,395 ^31.2

COPD (ICD: 490-492, 494, 496) ⁸¹⁶ ^2.1

Asthma (ICD: 493) ²⁸⁶ ^0.7

Age (years)

0 - 34 2,738 6.9

35-64 7,238 18.2

65-74 8,557 21.6

75-84 13,341 33.6

85+ 7,808 19.7

Gender

Men 21,529 54.3

Women 18,153 45.7

Season of hospital admission

Cold (October-March) 21,674 54.6

Warm (April-September) 18,008 45.4

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26 Table 4a. Effect of PAH on hospital admissions, by disease, lag and model used for exposure assessment. Results expressed as % increases of risk (% IR) for IQR increases of the pollutant, calculated on the difference between cases and controls. PAH concentrations are expressed as ng/m³

Variables Cardiovascular Ischemicheartdisease Heartfailure Cerebrovascular Respiratory

% IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI PAH, case-base (IQR=1.56)

lag 0-1 0.30 -0.91 1.53 -0.49 -2.93 2.00 0.79 -1.90 3.56 1.26 -1.15 3.72 0.67 -1.03 2.40

lag 2-5 -0.35 -2.05 1.38 -1.80 -5.13 1.64 1.21 -2.67 5.25 -0.35 -3.27 2.66 -0.87 -3.22 1.54

lag 0-5 -0.05 -2.08 2.03 -2.19 -6.16 1.94 1.93 -2.71 6.79 1.79 -2.28 6.03 -0.20 -3.04 2.72

PAH, assimilated (IQR=0.64)

lag 0-1 0.21 -0.88 1.31 -0.58 -2.76 1.65 0.80 -1.62 3.27 1.48 -0.69 3.70 0.85 -0.71 2.44

lag 2-5 0.39 -1.00 1.80 -0.03 -2.79 2.81 0.34 -2.79 3.57 1.23 -1.28 3.80 -1.04 -2.97 0.93

lag 0-5 0.55 -1.13 2.25 -0.48 -3.81 2.96 0.99 -2.79 4.90 2.60 -0.61 5.92 -0.36 -2.71 2.05

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27 Table 4b. Effect of BaP on hospital admissions, by disease, lag and model used for exposure assessment. Results expressed as % increases of risk (% IR) for IQR increases of the pollutant, calculated on the difference between cases and controls. BaP concentrations are expressed as ng/m³

% IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI BaP, case-base (IQR=0.45)

lag 0-1 0.28 -0.88 1.45 -0.50 -2.82 1.89 0.71 -1.87 3.35 1.21 -1.09 3.56 0.63 -1.00 2.29

lag 2-5 -0.35 -1.98 1.31 -1.68 -4.87 1.62 1.13 -2.60 4.99 -0.44 -3.22 2.41 -0.85 -3.11 1.45

lag 0-5 -0.07 -2.04 1.93 -2.11 -5.95 1.88 1.79 -2.68 6.48 1.64 -2.28 5.72 -0.23 -2.97 2.59

BaP, assimilated (IQR=0.16)

lag 0-1 0.17 -0.89 1.24 -0.61 -2.72 1.55 0.69 -1.65 3.08 1.54 -0.57 3.69 0.84 -0.68 2.39

lag 2-5 0.44 -0.88 1.78 0.29 -2.34 3.00 0.15 -2.81 3.20 1.29 -1.07 3.71 -1.10 -2.93 0.77

lag 0-5 0.57 -1.04 2.21 -0.18 -3.38 3.13 0.70 -2.91 4.43 2.27 -0.64 5.26 -0.48 -2.73 1.83

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28 Table 4c. Effect of PM2.5 on hospital admissions, by disease, lag and model/source used for exposure assessment. Results expressed as % increases of risk (% IR) for IQR increases of the pollutant, calculated on the difference between cases and controls. PM2.5 concentrations are expressed as µg/m³

% IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI % IR 95% CI PM2.5, case-base (IQR=13.23)

lag 0-1 2.48 -0.79 5.86 1.92 -4.52 8.79 2.59 -4.72 10.46 5.92 -0.59 12.86 -0.04 -4.57 4.71

lag 2-5 -0.11 -3.64 3.54 -1.31 -8.23 6.14 -2.01 -9.69 6.32 2.49 -4.40 9.88 1.85 -3.18 7.14

lag 0-5 1.33 -2.93 5.78 -0.13 -8.46 8.96 -0.45 -9.67 9.72 5.69 -2.75 14.87 1.76 -4.25 8.16

PM2.5, ARPA monitors (IQR=14.00)

lag 0-1 2.00 -1.44 5.57 1.98 -4.78 9.22 1.56 -6.11 9.85 5.44 -1.38 12.72 -0.05 -4.80 4.93

lag 2-5 0.34 -3.32 4.13 -0.30 -7.52 7.48 -1.10 -9.04 7.52 2.29 -4.87 9.99 2.86 -2.37 8.37

lag 0-5 1.43 -3.04 6.11 0.74 -8.05 10.37 -0.35 -9.98 10.31 5.47 -3.46 15.23 2.79 -3.56 9.56

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29 On the basis of previous results, it was difficult to identify a “reference” lag for the association between PAH and disease-specific hospitalizations. Since the highest estimates for total cardiovascular and total respiratory diseases were found at immediate latencies, the lag 0-1 was chosen. Consistently with the mortality analysis, further analyses on effect modification and concentration-response were carried on for PAH only.

Table 5 report the results of effect modification analysis, showing highest effects of PAH (case- base, lag 0-1) on cardio-respiratory hospitalizations in women and in subjects admitted to a hospital during the warmer season, with no clear differences across age classes.

Table 5. Effects of PAH (lag 0-1, case-base) on cardio-respiratory hospitalizations, by individual characteristics. Results expressed as % increases of risk (% IR) for IQR=1.56 ng/m³ increases of the pollutant

Variables

Effects per 1.56 ng/m³

% IR 95% CI P int

Overall ^0.40 ^-0.59 ^1.39 ^-

Age

0-64 ^0.04 ^-1.86 ^1.98 ^- 65-84 ^0.59 ^-0.70 ^1.90 ^0.634

85+ ^0.30 ^-1.75 ^2.39 ^0.857

Gender

Males ^-0.23 ^-1.52 ^1.09 ^- Females ^1.14 ^-0.28 ^2.57 ^0.154 Season of hospitalization

Cold (October-March) ^0.37 ^-0.62 ^1.37 ^- Warm (April-September) ^7.84 ^-5.43 ^22.96 ^0.285

In bold, results statistically significant at 95% level (95% CI not overlapping 0)

Figure 1 displays the concentration-response relationships between disease-specific hospitalizations and PAH (case-base, lag 0-1) exposure. No association was found between PAH and any of the outcomes, with a suggestion of linear relationship only with COPD admissions.

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30 Figure 1. Concentration-response curves of PAH (lag 0-1, case-base) and disease-specific hospitalizations. PAH concentrations are expressed as ng/m³

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31 3.4 Conclusions

We found little evidence of an association of any of the exposures with cardio-respiratory emergency hospitalizations. There was a weak suggestion of immediate association of PM2.5 with cardiovascular morbidity, and prolonged effect on respiratory morbidity. No association was found for PAH and BaP with any of the studied outcomes, however it is worth to underline that COPD admissions increased with PAH levels.

This is one of the few studies investigating the short-term association between PAH and morbidity.

The present study did not support any relationship, but the limited statistical power prevents any conclusion on this regard. More studies are therefore needed to replicate these findings, possibly in other settings and using longer time-series.

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32 3.5 References

1. Brook RD, Rajagopalan S, Pope CA 3rd, et al. American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010; 121:2331-78.

2. Ruckerl R, Schneider A, Breitner S, et al. Health effects of particulate air pollution: a review of epidemiological evidence. Inhalation Toxicology, 2011; 23: 555-592.

3. Beelen R, Raaschou-Nielsen O, Stafoggia M, et al. Effects of long-term exposure to air pollution on natural cause mortality: an analysis of 22 European cohorts within the multi-center ESCAPE project. Lancet, 2014; 383: 785-795.

4. Cesaroni G, Badaloni C, Gariazzo C, et al. Long-term exposure to urban air pollution and mortality in a cohort of more than a million adults in Rome. Environ Health Perspect.

2013;121:324-31.

5. Cesaroni G, Forastiere F, Stafoggia M, et al. Long-term exposure to ambient air pollution and incidence of acute coronary events -Analysis of eleven European cohorts from the ESCAPE Project. BMJ. 2014 Jan 21;348:f7412. doi: 10.1136/bmj.f7412.

6. Stafoggia M, Cesaroni G, Peters A, et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events – Results from eleven European cohorts within the ESCAPE Project. Environ Health Perspect. 2014 May 16. [Epub ahead of print]

7. Raaschou-Nielsen O, Andersen ZJ, Beelen R, et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). Lancet Oncol. 2013;14:813-22. doi: 10.1016/S1470-2045(13)70279-1.

8. IARC: Outdoor air pollution a leading environmental cause of cancer deaths. Press Release 221.

17 October 2013.(available at http://www.iarc.fr/en/media-centre/pr/2013/pdfs/pr221_E.pdf)