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Risk of leukaemia and residential exposure to air pollution in an industrial area in Northern Italy: a case-control study a
b
c
c
Stefano Parodi , Irene Santi , Claudia Casella , Antonella Puppo , d
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Fabio Montanaro , Vincenzo Fontana , Massimiliano Pescetto & e
Emanuele Stagnaro a
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Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy b
Clinical Investigation and Documentation, AO Foundation, Dübendorf, Switzerland c
Liguria Region Tumour Registry, IRCCS AOU San Martino-IST, Genoa, Italy d
Management and Statistics Unit, Latis Contract Research Organization, Genoa, Italy e
Unit of Epidemiology, Biostatistics and Clinical Trials, IRCCS AOU San Martino-IST, Genoa, Italy f
Savona Province Department – AFIA Sector, Liguria Environmental Protection Agency, Savona, Italy Published online: 23 Sep 2014.
To cite this article: Stefano Parodi, Irene Santi, Claudia Casella, Antonella Puppo, Fabio Montanaro, Vincenzo Fontana, Massimiliano Pescetto & Emanuele Stagnaro (2015) Risk of leukaemia and residential exposure to air pollution in an industrial area in Northern Italy: a case-control study, International Journal of Environmental Health Research, 25:4, 393-404, DOI: 10.1080/09603123.2014.958136 To link to this article: http://dx.doi.org/10.1080/09603123.2014.958136
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International Journal of Environmental Health Research, 2015 Vol. 25, No. 4, 393–404, http://dx.doi.org/10.1080/09603123.2014.958136
Risk of leukaemia and residential exposure to air pollution in an industrial area in Northern Italy: a case-control study
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Stefano Parodia, Irene Santib, Claudia Casellac, Antonella Puppoc, Fabio Montanarod, Vincenzo Fontanae, Massimiliano Pescettof and Emanuele Stagnaroe* a Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy; bClinical Investigation and Documentation, AO Foundation, Dübendorf, Switzerland; cLiguria Region Tumour Registry, IRCCS AOU San Martino-IST, Genoa, Italy; dManagement and Statistics Unit, Latis Contract Research Organization, Genoa, Italy; e Unit of Epidemiology, Biostatistics and Clinical Trials, IRCCS AOU San Martino-IST, Genoa, Italy; fSavona Province Department – AFIA Sector, Liguria Environmental Protection Agency, Savona, Italy
(Received 21 March 2014; final version received 20 June 2014) Leukaemia risk in adult populations exposed to environmental air pollution is poorly investigated. We have carried out a population-based case-control study in an area that included a fossil fuel power plant, a coke oven and two big chemical industries. Information on residential history and several risk factors for leukaemia was obtained from 164 cases, diagnosed between 2002 and 2005, and 279 controls. A higher risk for subjects residing in polluted areas was observed, but statistical significance was not reached (adjusted OR = 1.11 and 1.56 for subjects living in moderately and in heavily polluted zones, respectively, p = 0.190). Results suggest a possible aetiological role of residential air pollution from industrial sites on the risk of developing leukaemia in adult populations. However, the proportion of eligible subjects excluded from the study and the lack of any measure of air pollution prevent definitive conclusions from being drawn. Keywords: leukaemia; air pollution; industrial pollution; case-control study; environmental risk
Introduction Leukaemia is a haematopoietic cancer, responsible in Italy for 15.4 and 12.2 new cases per 100,000 inhabitants per year, respectively, in males and in females (AIRTUM 2009). Little is known about the aetiology of leukaemia. Recognized or suspected causes include: smoking habit, viral infections, exposure to radiation, chemotherapeutic agents, electromagnetic fields, pesticides, benzene and other hydrocarbons (Ilhan et al. 2006; Zur Hausen 2009; Carpenter 2010). Urban and industrial air pollutants contain several recognized carcinogens, including inorganic particulates, radionuclides, dioxins, benzene and polycyclic aromatic hydrocarbons (Cohen 2000). Among industrial facilities, solid waste incinerators, coke ovens and fossil fuel power plants have been recognized as major sources of outdoor carcinogens (Cohen 2000).
*Corresponding author. Email: [email protected] © 2014 Taylor & Francis
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Residential exposure to air pollutants have been found to be associated with leukaemia risk in many investigations, albeit with some contradictory results (Raaschou-Nielsen et al. 2001; Parodi et al. 2003; Yu et al. 2006; Marinaccio et al. 2011). The large majority of studies was conducted in children, while investigations on adult populations are rarer and based on ecological design (García-Pérez et al. 2010; Talbott et al. 2011; García-Pérez et al. 2013) with few exceptions (Yu et al. 2006; Wong et al. 2009). The present study aims to investigate the risk of leukaemia in an area in Northern Italy, characterized by an intense industrial activity, using a population based case-control approach.
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Methods Study area Savona Province (SP) in Northern Italy includes a large industrial area where many facilities are located near the residential zone. The most relevant sources of air pollutants are a fossil fuel power plant, a coke oven and two big chemical industries (Figure 1 and Table 1). In more detail, such industries were identified as the major sources of air pollutants, for the following reasons: the chemical industry in municipality n. 26 was responsible of a severe environmental contamination of air, water and soil (Senato della Repubblica Italiana 1992; Semenzin et al. 2008, 2009), eventually leading to its inclusion among the 14 sites of national interest to be reclaimed by an Italian Parliament law in 1998; the second chemical industry, located in municipality n. 15, was selected due to its large dimension and the production of many toxic compounds; finally, the coke oven (located in the same municipality, n. 15) and the fossil fuel power plant (located in municipality n. 52) are recognized to be among the major sources of a large variety of carcinogenic compounds (Cohen 2000). All these facilities are still active, except for the industry located in the northern side of town n. 23 that was dismantled in 1999. Furthermore, some other factories are located in the SP, including three glassworks in towns n. 5, n. 18 and n. 27, a company for treating solid urban waste and a chemical industry mainly involved in the production of fertilizers, both located in town n. 64 (Figure 1 and Table 1). About 272,500 persons were resident in SP (ISTAT 2001), including 34.3 % in rural or semi-rural municipalities, and the remaining 65.7 % in urban or semi-urban areas (Figure 1). Study subjects A population-based case-control study was carried out in SP to investigate the risk of leukaemia and non-Hodgkin’s lymphoma (NHL) in association with air pollution from industrial sources. The present article is focused on the analysis of leukaemia risk, whereas the corresponding estimates for NHL have been reported and discussed in another paper (Parodi et al. 2014). Leukaemia cases were defined as subjects with a new diagnosis of leukaemia, based on the ICD9 code 204.0–208.9. Moreover, malignancies of the haematopoietic system of uncertain behaviour (ICD9 code 238.4 and 238.7) were also included in the study. Incident cases were identified through hospital records from the Ligurian Region Hospital Discharge database. Eligibility criteria for the cases were: diagnosis of leukaemia between 2002 and 2005, age ≥ 20 years at the time of diagnosis and
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Figure 1. Map of the study area (Savona Province) with the indication of the major sources of industrial air pollution. Adapted from Parodi et al. (2014) with permission. Note: List of municipalities in the SP: (R) = rural/semi-rural; (U) = urban/semi-urban. 1 = Alassio (U); 2 = Albenga (U); 3 = Albissola Marina (U); 4 = Albisola Superiore (U); 5 = Altare (U); 6 = Andora (U); 7 = Arnasco (R); 8 = Balestrino (R); 9 = Bardineto (R); 10 = Bergeggi (U); 11 = Boissano (U); 12 = Borghetto Santo Spirito (U); 13 = Borgio Verezzi (U); 14 = Bormida (R); 15 = Cairo Montenotte (U); 16 = Calice Ligure (R); 17 = Calizzano (R); 18 = Carcare (U); 19 = Casanova Lerrone (R); 20 = Castelbianco (R); 21 = Castelvecchio di Rocca Barbena (R); 22 = Celle Ligure (U); 23 = Cengio (U); 24 = Ceriale (U); 25 = Cisano sul Neva (R); 26 = Cosseria (R); 27 = Dego (R); 28 = Erli (R); 29 = Finale Ligure (U); 30 = Garlenda (U); 31 = Giustenice (R); 32 = Giusvalla (R); 33 = Laigueglia (U); 34 = Loano (U); 35 = Magliolo (R); 36 = Mallare (R); 37 = Massimino (R); 38 = Millesimo (U); 39 = Mioglia (R); 40 = Murialdo (R); 41 = Nasino (R); 42 = Noli (U); 43 = Onzo (R); 44 = Orco Feglino (R); 45 = Ortovero (R); 46 = Osiglia (R); 47 = Pallare (R); 48 = Piana Crixia (R); 49 = Pietra Ligure (U); 50 = Plodio (R); 51 = Pontinvrea (R); 52 = Quiliano (U); 53 = Rialto (R); 54 = Roccavignale (R); 55 = Sassello (R); 56 = Savona (U); 57 = Spotorno (U); 58 = Stella (R); 59 = Stellanello (R); 60 = Testico (R); 61 = Toirano (U); 62 = Tovo San Giacomo (R); 63 = Urbe (R); 64 = Vado Ligure (U); 65 = Varazze (U); 66 = Vendone (R); 67 = Vezzi Portio (R); 68 = Villanova d’Albenga (U); 69 = Zuccarello (R).
residence of at least five years in the same place of SP. Controls were frequently matched by sex and age (± 5 years) had to be unaffected by leukaemia or lymphoma at the time of their recruitment (15 April 2004) and were randomly selected from the regional population health registry using the same eligibility criteria. Questionnaire and interview Information about education, residential history, lifestyle habits (smoking, hair dye use, alcohol consumption and leisure activities), occupational exposures, previous diseases,
Cairo Montenotte (n. 15)
Quiliano (n. 52)
Altare (n. 5)
Carcare (n. 18)
Dego (n. 27)
Vado Ligure (n. 64)
Vado Ligure (n. 64)
Coke oven
Power plant
Other factories Glasswork
Glasswork
Glasswork
Solid waste treatment
Chemical industry
*Numbered according to Figure 1. # Including related compounds.
WWF (2013)
Cairo Montenotte (n. 15)
Chemical Industry
WWF (2013), (2007) WWF (2013), (2007) WWF (2013), (2007) WWF (2013), (2007) WWF (2013), (2007) ARPAT
ARPAT
ARPAT
ARPAT
ARPAT
ARPAT (2007)
ARPAT (2007)
Semenzin et al. (2008)
Reference
Major factories Chemical Industry Cengio (n. 23) (dismantled in 1999)
Municipality*
Main industrial activities in the SP (Northern Italy).
Industrial plants
Table 1.
NH3, inorganic acids, fertilizers
Plaster, asphalt, concrete, cement, glass fibre, tiles, ceramics products Plaster, asphalt, concrete, cement, glass fibre, tiles, ceramics products Plaster, asphalt, concrete, cement, glass fibre, tiles, ceramics products Waste dumping
Fluorescent dyes, imaging compounds, starting materials for APIs, other fine chemicals, electronic materials Coke oven products (coke, ammonium sulphate, tar, sulphur) Electrical energy Electrical energy
Lighting gas, nitric acid, phenol, sulphuric acid, dyes and pigments, β-naphthol, phtalocyanine
Products
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Zn#
CH4
NOX, PM10
NOX, PM10
NOX, PM10
CO2, N2O, NH3, NOX, SOX, As#, Cd#, Cu#, Hg#, Ni#, Dioxin, Furans, HCl, HF, PM10
CO2, NOX, benzene, PAH, PM10
Tetrachloroethane, benzene, chlorobenzene, nitrobenzene, naphthalene, ethyl-benzene, perchloroethylene, dioxin PM10, NOX
Main air pollutants
396 S. Parodi et al.
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medications and family medical history was obtained by a standardized structured questionnaire. A person-to-person interview was made by trained interviewers, either face-to-face (63 %) or by telephone (37 %). Most interviews (71.3 %) were directly administered, while 28.7 % were addressed to one of their relatives (preferably a next-of-kin). The large majority of interviews (90.1 %) were either made at home (50.1 %) or arranged in an office of the Savona Local Health Centre 2 (44.9 %); only two interviews were administered to hospitalized persons (0.5 %). Interviews took place between April 2005 and February 2009 and lasted, on average, 81 min (SD = 20.4).
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Exposure assessment On the basis of the presence of industrial activities, the degree of urbanization and the two directions of prevailing winds (from January to November and in December), three exposure areas were identified in SP (Figure 1): red zone (52,813 inhabitants at the 2001 Italian national census), considered as highly exposed; grey zone (181,816 inhabitants), at an intermediate exposure; and green zone (37,899 inhabitants), very slightly or not exposed. In more detail, wind direction during each month was assessed using data from the airport located in municipality n. 2 (Figure 1), and it was defined as the prevalent if it was the most frequently observed in a specific month during the observation period (1973–2013; Il Meteo 2014). Red zone was defined as the set of municipalities that: (a) included at least one out of the four major industrial plants; or (b) were downwind of at least one major facility, according to the direction of the prevailing winds; or (c) included an urban or semi-urban area and at least one industrial activity; or (d) included more than one industrial activity. Grey zone consisted of urban or semi-urban areas not downwind of the four major industrial plants, which included up to one small factory. Finally, green zone included only rural or semi-rural areas without any industrial facility and not downwind of any industrial activity. Municipalities were considered as either rural/semi-rural or urban/semi-urban according to the Italian Central Statistical Institute official classification system (ISTAT 1986). Direct assessment of environmental exposure was hampered by the lack of information of air pollutant concentrations in the period preceding our investigation. Moreover, few monitoring stations are currently located in SP and only one in the green zone (town n. 4, Figure 1). A detailed description of air pollutant concentrations in SP for the period 1997–2010 has been reported in a previous paper (Parodi et al. 2014). Original raw data (hourly mean concentrations per day) are available at the Regional Environmental Protection Agency website (ARPAL 2005). Statistical analysis Association between environmental exposure and leukaemia risk was evaluated by binomial logistic regression model (Hosmer & Lemeshow 2000). Adjusted ORs were estimated including in the models the following putative confounders: type of interview, gender, age, smoking habit, educational level and exposure to radiation, pesticides or aromatic hydrocarbons. In more detail, type of interview was coded as personal, when the subject was personally interviewed, and as not personal if a relative answered the questionnaire. Age was included in the model as a continuous variable, using both a linear and a quadratic term. Exposure to tobacco smoking was categorized into three
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groups (ever, former and never smokers, respectively), while dose–response relationship was assessed including the average number of pack-years as nested variables, according to the method proposed by Leffondré et al. (2002). Educational level was considered as low for subjects with no diploma or only a primary school diploma, intermediate for those who obtained a lower class diploma and high for persons with high school diploma or university degree. Exposure to pesticides, hydrocarbons and radiations were included into the model as binary indicators (present/absent). Professional exposure to pesticide or hydrocarbons was assessed using the ISTAT-ATECO codes (ISTAT 1991) and an individual was considered as exposed if she/he performed a job in an exposed category for at least five years during her/his life. Finally, radiation exposure included professional, therapeutic and diagnostic ones; for these latter, only heavy radiations (namely, computerized axial tomography, scintigraphy and radioscopy) were taken into consideration. Simple logistic regression (Model I) and two multiple logistic regression models that provided ORs adjusted either for age and gender (Model II) or for all considered confounders (Model III) were fitted and compared. An analysis by cumulative exposure, defined as the length of residence in the most polluted area (red zone), was also carried out. All statistical tests were two sided and a p value lower than 0.05 was considered statistically significant. All analyses were performed by the statistical package Stata (version 11.1, Stata Corporation, College Station, TX).
Results Table 2 reports recent measures (period 2011–2012) of air pollution in SP. PM10 concentrations tended to be higher in the red zone than in the other two areas, except than in town n. 23 which corresponds to the dismantled factory. The pattern of PM2.5 levels was consistent with that of PM10, but no data for the green zone were available. Benzene concentrations were registered in three monitoring stations and were slightly higher in the red and grey zones. SO2 showed a heterogeneous pattern, while NO2 was very high in two stations in the red zone (towns n. 64 and n. 18) which are downwind to two big factories, a power plant and a coke oven, respectively, but not in the three remaining locations. Missing data for PM10 and PM2.5 were above 5 % for any considered station, except that in municipalities n. 2 and n. 18, in which they were about 10 %; missing data for benzene ranged from 3 and 7 %, for SO2 between 3 and 13 % and for NO2 between 1 and 9 %. With regard to previous measures of air pollution, data about benzene, NO2 and SO2 concentrations in the green zone, available since 2006 only, showed a pattern consistent with that reported in Table 2 (Parodi et al. 2014). In the area under study, 755 subjects were originally recruited. Among them, 5 (2 cases and 3 controls) were excluded because of not actually residing in the SP; 11 (2 cases and 9 controls) because they had been residing for less than 5 years; and 61 (6 cases and 55 controls) because their residence was not successfully located. Among the 678 eligible (237 leukaemia cases and 441 controls), 443 (164 cases and 279 controls) entered the study, while 235 were not interviewed for the following reasons: 40 (18 cases and 22 controls) died before the interview and no living relative was traced, 12 (4 cases and 8 controls) were affected by some physic or psychic impairments, 32 (23 cases and 9 controls) died before the interview and their relatives refused to participate in the study and, finally, 151 (28 cases and 123 controls) refused the interview.
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Table 2. Mean annual concentrations and standard deviations (in brackets) of some selected air pollutants measured in the SP in 2011–2012. Approximate location of monitoring stations is reported in Figure 1. Air pollutants (μg/m3) 2011 PM10
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Red zone Quiliano (n. 52)
24.6* (2.6) Vado L. (n. 64) 27.8* (3.5) Cairo M. (n. 15) 36.0* (12.2) Carcare (n. 18) 22.1* (12.3) Cengio (n. 23) 16.9* (3.5) Grey zone Savona (n. 56) Savona (n. 56) Albenga (n. 2) Green zone Albisola S. (n. 4)
14.1 (4.5) 21.4* (2.0) 21.5* (0.98) 15.3* (3.5)
PM2.5
2012
C6H6
SO2
17.0* n.a. (2.8) 18.9* 3.0 (4.3) (0.71) 23.8* n.a. (10.4) n.a. n.a.
n.a.
n.a.
n.a.
n.a.
2.9 (0.54) 14.4* n.a. (2.4) 14.9* n.a. (1.5) n.a.
1.4 (0.22)
NO2
PM10 PM2.5
19.7 34.4 (5.5) (11.2) 8.3 36.4 27.6 (1.3) (11.1) (5.1) n.a. 27.4 30.7 (9.0) (9.4) 23.9 46.4 27.4 (10.6) (19.8) (6.8) n.a. 15.4 11.1 (9.1) (5.0) n.a. 10.2 (2.4) 6 (1.6)
24.6 (6.7) 25.2 (4.4) 32.1 (5.3)
14.7 (3.4) 20.2 (3.7) 20.7 (3.5)
n.a.
n.a.
12.1 (3.4)
21.3 (6.5) 17.7 (4.3) 22.0 (7.4) n.a. n.a.
n.a. 13.4 (3.5) 14.2 (3.1) n.a.
C6H6
SO2
n.a.
n.a.
2.9 (0.81) n.a.
n.a.
NO2
18.7 (3.5) 2.8 5.9 36.6 (0.80) (1.3) (9.2) n.a. n.a. 21.1 (−7.0) n.a. 9.3 49.9 (3.6) (6.3) n.a. n.a. 17.4 (4.9)
n.a.
1.3 (0.29)
6.7 (6.0) 4.6 (1.0)
38.6 (9.5) 21.9 (3.4) 28.4 (6.1)
n.a.
n.a.
C6H6 = benzene; n.a. = not available; n = number of the host town reported in the legend of Figure 1. *Data available since 1 March 2011.
The proportion of interviewed subjects did not differ appreciably by zone of exposure (green: 61.1 %; grey: 59.3 %; red: 61.2 %; p = 0.885). No statistically significant difference was observed in the participation rate after stratification by case/control status (p = 0.212 among cases and p = 0.782 among controls, respectively). All potential confounders were similarly distributed among the two groups (Table 3), except education (18.3 % of cases had a high school diploma or university degree vs. 22.9 % of controls), even if statistical significance was not reached (p = 0.174, χ2 test). Moreover, controls were more likely to be personally interviewed (83.5 % vs. 50.6 %, p < 0.001). Lymphoblastic leukaemia was the most common type (n = 63), and included 7 acute, 49 chronic and 7 unspecified leukaemia. Myelocytic leukaemia included 58 cases (27 acute, 11 chronic and 20 unspecified). Finally, among the 43 other leukaemia, 2 cases were monocytic, 14 chronic erythremia, 9 megakaryocytic, 8 of unspecified cell type and 10 of uncertain behaviour. The small sample size prevented from performing subgroup analyses. Table 4 shows the estimates of association between residential exposure and risk of leukaemia. An excess risk was observed for the most polluted areas (OR = 1.11 for the grey zone and OR = 1.56 for the red zone compared to the green one, Model III), but statistical significance was not reached (p = 0.190). No trend in risk emerged from
400 Table 3.
S. Parodi et al. Characteristics of the 443 interviewed subjects by case-control status.
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Subject characteristics Age at recruitment Median age (IQR) Gender, N (%) Females Males Educational level, N (%) Low Intermediate High Tobacco smoking, N (%)* Never smokers Former smokers Current smokers Radiation exposure, N (%) Yes Not Pesticide exposure, N (%) Yes Not Aromatic hydrocarbon exposure, N (%) Yes Not Interview type, N (%) Subject Relative
Cases (164)
Controls (279)
72 (63–78)
71 (63–78)
72 (43.9) 92 (56.1)
120 (43.0) 159 (57.0)
92 (56.1) 42 (25.6) 30 (18.3)
131 (47.0) 84 (30.1) 64 (22.9)
92 (56.4) 49 (30.1) 22 (13.5)
153 (55.4) 73 (26.5) 50 (18.1)
33 (20.1) 131 (79.9)
59 (21.1) 220 (78.9)
18 (11.0) 146 (89.0)
37 (13.3) 242 (86.7)
42 (25.6) 122 (74.4)
56 (20.1) 223 (79.9)
83 (50.6) 81 (49.4)
233 (83.5) 46 (16.5)
p 0.942 0.855 0.174
0.400
0.797 0.481 0.175 30 years, compared with never resident, p = 0.435). Finally, when the length of residence was modelled as a continuous variable, no association was observed (p = 0.311, results not shown in Table 4). Discussion We found a small excess risk for residents in a heavily polluted zone of SP. Even if statistical significance was not reached, our estimates are consistent with those reported in a larger investigation in an area of southern Taiwan, Republic of China (Yu et al. 2006), that found a positive association between the exposure to industrial air pollution from some refineries, measured by an “opportunity” score, which corresponded to an OR = 1.5 for a residence of about three months at 1 km downwind of a petrochemical plant. However, in the present study, an effect of cumulative exposure was not found. This might be due to a lack of statistical power as a consequence of the small sample size of the exposed population. In fact, only 53 cases had resided in the red zone for at least one year during their life and, among them, only 15 were exposed for a period lower than 30 years, thus preventing a reliable analysis of dose–response trend. Apart from the smaller sample size, another difference between our investigation and that by Yu et al. (2006) is the higher heterogeneity of the sources of air pollutants in SP. Among them, the four big facilities included a fossil fuel power plant, which is a recognized source of
International Journal of Environmental Health Research Table 4. trend.
OR estimates by exposed areas, their related 95 % confidence intervals and test for
Exposed areas
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Exposure
Comparison by areas Model I Green zone Low Grey zone Mild Red zone High Model II Green zone Low Grey zone Mild Red zone High Model III* Green zone Low Grey zone Mild Red zone High Cumulative exposure Model I Red zone Never resident Red zone ≤ 30 years Red zone > 30 years Model II Red zone Never resident Red zone ≤ 30 years Red zone > 30 years Model III* Red zone Never resident Red zone ≤ 30 years Red zone > 30 years
Cases
Controls
OR
95 % CI
18 102 44
40 182 57
1 1.25 1.72
Ref 0.68–2.28 0.87–3.39
18 102 44
40 182 57
1 1.22 1.69
Ref 0.67–2.26 0.85–3.36
18 101 44
39 181 56
1 1.11 1.56
Ref 0.56–2.22 0.72–3.38
111 15 38
205 19 55
1 1.46 1.30
(ref) 0.71–2.98 0.79–2.05
111 15 38
205 19 55
1 1.42 1.29
(ref) 0.69–2.93 0.80–2.07
110 15 38
203 19 54
1 1.28 1.21
(ref) 0.58–2.82 0.70–2.08
p 0.093
0.099
0.190
0.253
0.241 0.435
Model I: unadjusted OR; Model II: OR adjusted for age and gender; Model III: OR adjusted for any considered confounder (namely, type of interview, gender, age, smoking habit, educational level, and exposure to radiation, pesticides or aromatic hydrocarbons). p = likelihood ratio test for trend. *4 missing data for smoking intensity.
carcinogenic air contaminants (Cohen 2000). Fumes were dispersed through two very tall stacks and their actual impact on resident population is not well assessed. The few measures of air pollutant concentrations in the municipality immediately downwind this facility (n. 64) provided a not fully consistent picture (Table 2), showing PM10 levels slightly higher than those observed in the grey zone, similar benzene concentrations, but very high NO2 levels. The value of SO2 concentration, which is a well-known index of industrial activity, was only slightly higher than that observed in the two stations of Savona municipality (n. 56) in the grey zone. Unfortunately, SO2 concentration in Quiliano (municipality n. 52), where the power plant is located, was not available. Furthermore, Savona town includes a rather large harbour that might have been an uncontrolled source of SO2 from shipping activities (ISPRA 2009). Further studies, adopting advanced simulation models of air pollutants concentrations, are needed in order to provide a more reliable assessment of industrial exposure to carcinogenetic compounds for the population residing in SP, especially in the areas surrounding the big power plant. With regard to the remaining three big factories, they included a coke oven, which represents a well-known source of many carcinogenic compounds at intensive local impact (Parodi et al. 2005; Tsai et al. 2008), a facility for the production of photographic films and equipment (located in the same municipality), and a big
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chemical industry dismantled in 1999, which produced a large variety of organic chemicals responsible of severe contamination of air, water and soil (Senato della Repubblica Italiana 1992; Esposito et al. 2002; Marengo et al. 2002; Semenzin et al. 2009, 2008). In particular, coke oven is a relevant source of benzene, but measures of this pollutant were not available. However, in both considered years, PM10, which represents another characteristic pollutant from coke oven activity (Cohen 2000; Parodi et al. 2005), was higher in the municipality were the facility was located (n. 15) than in the three monitoring stations located in the grey zone. Furthermore, the very high NO2 concentration in municipality n. 18, which is downwind to both the coke oven and a big chemical industry and also included a glasswork activity, is consistent with our knowledge about the kind of pollution from such industrial activities (Table 1). Finally, both PM10 and benzene concentrations were lower in the green zone than in the other two areas in both years. In spite of the lack of measures of air pollutant concentrations in most SP areas in past years (Parodi et al. 2014) and the observed heterogeneity of measures of pollutant concentrations, especially SO2, the few recent measures at our disposal (especially particulate matter and benzene) seems consistent with our definition of exposed zones. However, the lack of measures of personal exposure remains a major limit of our investigation, and some other uncontrolled sources of pollution might have biased our estimates. Among them, environmental exposure to pesticides from agricultural activity, which is more likely in the areas not exposed to industrial air pollution (i.e. all areas in the green zone and many areas in the grey zone), might have reduced the estimates of ORs by inducing a bias toward null. Moreover, leukaemia subtypes represent different malignant entities with a partially recognized different aetiology (Linet et al. 2006). As a consequence, our results, based on a pooled analysis, might be prone to some misclassification bias. However, industrial air pollution include a large variety of potential carcinogenic compounds and some previous investigations point out to an excess risk for all leukaemia types in environmentally exposed populations (Yu et al. 2006; Wong et al. 2009; García-Pérez et al. 2010). Cases and controls strongly differed by the proportion of the personal vs. proxy interview. Nevertheless, no relevant differences were observed comparing crude ORs with those adjusted by putative confounders, which included the type of interview. This result indicates that information about residential exposure did not differ between directly interviewed persons and their relatives, thus suggesting that residential history is not prone to severe recall bias. Moreover, confounders were distributed in a quite homogeneous proportion among cases and controls. In conclusion, our results are partly consistent with previous investigations indicating a possible role of industrial air pollutants on the risk of developing leukaemia in adults. The lack of a clear dose–response trend, the low statistical power and the poor measures of air pollutant concentrations demand caution in interpreting our findings and call for a more in-depth epidemiological investigation. Acknowledgements This work was supported by a grant provided by the Savona Local Health Centre 2 (ASL2 Savonese). We are deeply indebted with Maria Antonietta Gioioso, Giovanni Olivieri, Giuliano Chiappa, Virna Frumento, Claudio Boffa and Margherita Costa (Savona Local Health Centre 2, ASL2 Savonese), who carried out the interviews; Maria Paola Briata (ASL2 Savonese), who coordinated the interviewers activity; Matteo Puntoni (Galliera Hospital of Genoa) for data management; and Robin Nesbitt (Institute of Public Health, University of Heidelberg) for language revision.
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Risk of leukaemia and residential exposure to air pollution in an industrial area in Northern Italy: a case-control study a
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Stefano Parodi , Irene Santi , Claudia Casella , Antonella Puppo , d
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Fabio Montanaro , Vincenzo Fontana , Massimiliano Pescetto & e
Emanuele Stagnaro a
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Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy b
Clinical Investigation and Documentation, AO Foundation, Dübendorf, Switzerland c
Liguria Region Tumour Registry, IRCCS AOU San Martino-IST, Genoa, Italy d
Management and Statistics Unit, Latis Contract Research Organization, Genoa, Italy e
Unit of Epidemiology, Biostatistics and Clinical Trials, IRCCS AOU San Martino-IST, Genoa, Italy f
Savona Province Department – AFIA Sector, Liguria Environmental Protection Agency, Savona, Italy Published online: 23 Sep 2014.
To cite this article: Stefano Parodi, Irene Santi, Claudia Casella, Antonella Puppo, Fabio Montanaro, Vincenzo Fontana, Massimiliano Pescetto & Emanuele Stagnaro (2015) Risk of leukaemia and residential exposure to air pollution in an industrial area in Northern Italy: a case-control study, International Journal of Environmental Health Research, 25:4, 393-404, DOI: 10.1080/09603123.2014.958136 To link to this article: http://dx.doi.org/10.1080/09603123.2014.958136
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International Journal of Environmental Health Research, 2015 Vol. 25, No. 4, 393–404, http://dx.doi.org/10.1080/09603123.2014.958136
Risk of leukaemia and residential exposure to air pollution in an industrial area in Northern Italy: a case-control study
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Stefano Parodia, Irene Santib, Claudia Casellac, Antonella Puppoc, Fabio Montanarod, Vincenzo Fontanae, Massimiliano Pescettof and Emanuele Stagnaroe* a Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy; bClinical Investigation and Documentation, AO Foundation, Dübendorf, Switzerland; cLiguria Region Tumour Registry, IRCCS AOU San Martino-IST, Genoa, Italy; dManagement and Statistics Unit, Latis Contract Research Organization, Genoa, Italy; e Unit of Epidemiology, Biostatistics and Clinical Trials, IRCCS AOU San Martino-IST, Genoa, Italy; fSavona Province Department – AFIA Sector, Liguria Environmental Protection Agency, Savona, Italy
(Received 21 March 2014; final version received 20 June 2014) Leukaemia risk in adult populations exposed to environmental air pollution is poorly investigated. We have carried out a population-based case-control study in an area that included a fossil fuel power plant, a coke oven and two big chemical industries. Information on residential history and several risk factors for leukaemia was obtained from 164 cases, diagnosed between 2002 and 2005, and 279 controls. A higher risk for subjects residing in polluted areas was observed, but statistical significance was not reached (adjusted OR = 1.11 and 1.56 for subjects living in moderately and in heavily polluted zones, respectively, p = 0.190). Results suggest a possible aetiological role of residential air pollution from industrial sites on the risk of developing leukaemia in adult populations. However, the proportion of eligible subjects excluded from the study and the lack of any measure of air pollution prevent definitive conclusions from being drawn. Keywords: leukaemia; air pollution; industrial pollution; case-control study; environmental risk
Introduction Leukaemia is a haematopoietic cancer, responsible in Italy for 15.4 and 12.2 new cases per 100,000 inhabitants per year, respectively, in males and in females (AIRTUM 2009). Little is known about the aetiology of leukaemia. Recognized or suspected causes include: smoking habit, viral infections, exposure to radiation, chemotherapeutic agents, electromagnetic fields, pesticides, benzene and other hydrocarbons (Ilhan et al. 2006; Zur Hausen 2009; Carpenter 2010). Urban and industrial air pollutants contain several recognized carcinogens, including inorganic particulates, radionuclides, dioxins, benzene and polycyclic aromatic hydrocarbons (Cohen 2000). Among industrial facilities, solid waste incinerators, coke ovens and fossil fuel power plants have been recognized as major sources of outdoor carcinogens (Cohen 2000).
*Corresponding author. Email: [email protected] © 2014 Taylor & Francis
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Residential exposure to air pollutants have been found to be associated with leukaemia risk in many investigations, albeit with some contradictory results (Raaschou-Nielsen et al. 2001; Parodi et al. 2003; Yu et al. 2006; Marinaccio et al. 2011). The large majority of studies was conducted in children, while investigations on adult populations are rarer and based on ecological design (García-Pérez et al. 2010; Talbott et al. 2011; García-Pérez et al. 2013) with few exceptions (Yu et al. 2006; Wong et al. 2009). The present study aims to investigate the risk of leukaemia in an area in Northern Italy, characterized by an intense industrial activity, using a population based case-control approach.
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Methods Study area Savona Province (SP) in Northern Italy includes a large industrial area where many facilities are located near the residential zone. The most relevant sources of air pollutants are a fossil fuel power plant, a coke oven and two big chemical industries (Figure 1 and Table 1). In more detail, such industries were identified as the major sources of air pollutants, for the following reasons: the chemical industry in municipality n. 26 was responsible of a severe environmental contamination of air, water and soil (Senato della Repubblica Italiana 1992; Semenzin et al. 2008, 2009), eventually leading to its inclusion among the 14 sites of national interest to be reclaimed by an Italian Parliament law in 1998; the second chemical industry, located in municipality n. 15, was selected due to its large dimension and the production of many toxic compounds; finally, the coke oven (located in the same municipality, n. 15) and the fossil fuel power plant (located in municipality n. 52) are recognized to be among the major sources of a large variety of carcinogenic compounds (Cohen 2000). All these facilities are still active, except for the industry located in the northern side of town n. 23 that was dismantled in 1999. Furthermore, some other factories are located in the SP, including three glassworks in towns n. 5, n. 18 and n. 27, a company for treating solid urban waste and a chemical industry mainly involved in the production of fertilizers, both located in town n. 64 (Figure 1 and Table 1). About 272,500 persons were resident in SP (ISTAT 2001), including 34.3 % in rural or semi-rural municipalities, and the remaining 65.7 % in urban or semi-urban areas (Figure 1). Study subjects A population-based case-control study was carried out in SP to investigate the risk of leukaemia and non-Hodgkin’s lymphoma (NHL) in association with air pollution from industrial sources. The present article is focused on the analysis of leukaemia risk, whereas the corresponding estimates for NHL have been reported and discussed in another paper (Parodi et al. 2014). Leukaemia cases were defined as subjects with a new diagnosis of leukaemia, based on the ICD9 code 204.0–208.9. Moreover, malignancies of the haematopoietic system of uncertain behaviour (ICD9 code 238.4 and 238.7) were also included in the study. Incident cases were identified through hospital records from the Ligurian Region Hospital Discharge database. Eligibility criteria for the cases were: diagnosis of leukaemia between 2002 and 2005, age ≥ 20 years at the time of diagnosis and
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Figure 1. Map of the study area (Savona Province) with the indication of the major sources of industrial air pollution. Adapted from Parodi et al. (2014) with permission. Note: List of municipalities in the SP: (R) = rural/semi-rural; (U) = urban/semi-urban. 1 = Alassio (U); 2 = Albenga (U); 3 = Albissola Marina (U); 4 = Albisola Superiore (U); 5 = Altare (U); 6 = Andora (U); 7 = Arnasco (R); 8 = Balestrino (R); 9 = Bardineto (R); 10 = Bergeggi (U); 11 = Boissano (U); 12 = Borghetto Santo Spirito (U); 13 = Borgio Verezzi (U); 14 = Bormida (R); 15 = Cairo Montenotte (U); 16 = Calice Ligure (R); 17 = Calizzano (R); 18 = Carcare (U); 19 = Casanova Lerrone (R); 20 = Castelbianco (R); 21 = Castelvecchio di Rocca Barbena (R); 22 = Celle Ligure (U); 23 = Cengio (U); 24 = Ceriale (U); 25 = Cisano sul Neva (R); 26 = Cosseria (R); 27 = Dego (R); 28 = Erli (R); 29 = Finale Ligure (U); 30 = Garlenda (U); 31 = Giustenice (R); 32 = Giusvalla (R); 33 = Laigueglia (U); 34 = Loano (U); 35 = Magliolo (R); 36 = Mallare (R); 37 = Massimino (R); 38 = Millesimo (U); 39 = Mioglia (R); 40 = Murialdo (R); 41 = Nasino (R); 42 = Noli (U); 43 = Onzo (R); 44 = Orco Feglino (R); 45 = Ortovero (R); 46 = Osiglia (R); 47 = Pallare (R); 48 = Piana Crixia (R); 49 = Pietra Ligure (U); 50 = Plodio (R); 51 = Pontinvrea (R); 52 = Quiliano (U); 53 = Rialto (R); 54 = Roccavignale (R); 55 = Sassello (R); 56 = Savona (U); 57 = Spotorno (U); 58 = Stella (R); 59 = Stellanello (R); 60 = Testico (R); 61 = Toirano (U); 62 = Tovo San Giacomo (R); 63 = Urbe (R); 64 = Vado Ligure (U); 65 = Varazze (U); 66 = Vendone (R); 67 = Vezzi Portio (R); 68 = Villanova d’Albenga (U); 69 = Zuccarello (R).
residence of at least five years in the same place of SP. Controls were frequently matched by sex and age (± 5 years) had to be unaffected by leukaemia or lymphoma at the time of their recruitment (15 April 2004) and were randomly selected from the regional population health registry using the same eligibility criteria. Questionnaire and interview Information about education, residential history, lifestyle habits (smoking, hair dye use, alcohol consumption and leisure activities), occupational exposures, previous diseases,
Cairo Montenotte (n. 15)
Quiliano (n. 52)
Altare (n. 5)
Carcare (n. 18)
Dego (n. 27)
Vado Ligure (n. 64)
Vado Ligure (n. 64)
Coke oven
Power plant
Other factories Glasswork
Glasswork
Glasswork
Solid waste treatment
Chemical industry
*Numbered according to Figure 1. # Including related compounds.
WWF (2013)
Cairo Montenotte (n. 15)
Chemical Industry
WWF (2013), (2007) WWF (2013), (2007) WWF (2013), (2007) WWF (2013), (2007) WWF (2013), (2007) ARPAT
ARPAT
ARPAT
ARPAT
ARPAT
ARPAT (2007)
ARPAT (2007)
Semenzin et al. (2008)
Reference
Major factories Chemical Industry Cengio (n. 23) (dismantled in 1999)
Municipality*
Main industrial activities in the SP (Northern Italy).
Industrial plants
Table 1.
NH3, inorganic acids, fertilizers
Plaster, asphalt, concrete, cement, glass fibre, tiles, ceramics products Plaster, asphalt, concrete, cement, glass fibre, tiles, ceramics products Plaster, asphalt, concrete, cement, glass fibre, tiles, ceramics products Waste dumping
Fluorescent dyes, imaging compounds, starting materials for APIs, other fine chemicals, electronic materials Coke oven products (coke, ammonium sulphate, tar, sulphur) Electrical energy Electrical energy
Lighting gas, nitric acid, phenol, sulphuric acid, dyes and pigments, β-naphthol, phtalocyanine
Products
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Zn#
CH4
NOX, PM10
NOX, PM10
NOX, PM10
CO2, N2O, NH3, NOX, SOX, As#, Cd#, Cu#, Hg#, Ni#, Dioxin, Furans, HCl, HF, PM10
CO2, NOX, benzene, PAH, PM10
Tetrachloroethane, benzene, chlorobenzene, nitrobenzene, naphthalene, ethyl-benzene, perchloroethylene, dioxin PM10, NOX
Main air pollutants
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medications and family medical history was obtained by a standardized structured questionnaire. A person-to-person interview was made by trained interviewers, either face-to-face (63 %) or by telephone (37 %). Most interviews (71.3 %) were directly administered, while 28.7 % were addressed to one of their relatives (preferably a next-of-kin). The large majority of interviews (90.1 %) were either made at home (50.1 %) or arranged in an office of the Savona Local Health Centre 2 (44.9 %); only two interviews were administered to hospitalized persons (0.5 %). Interviews took place between April 2005 and February 2009 and lasted, on average, 81 min (SD = 20.4).
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Exposure assessment On the basis of the presence of industrial activities, the degree of urbanization and the two directions of prevailing winds (from January to November and in December), three exposure areas were identified in SP (Figure 1): red zone (52,813 inhabitants at the 2001 Italian national census), considered as highly exposed; grey zone (181,816 inhabitants), at an intermediate exposure; and green zone (37,899 inhabitants), very slightly or not exposed. In more detail, wind direction during each month was assessed using data from the airport located in municipality n. 2 (Figure 1), and it was defined as the prevalent if it was the most frequently observed in a specific month during the observation period (1973–2013; Il Meteo 2014). Red zone was defined as the set of municipalities that: (a) included at least one out of the four major industrial plants; or (b) were downwind of at least one major facility, according to the direction of the prevailing winds; or (c) included an urban or semi-urban area and at least one industrial activity; or (d) included more than one industrial activity. Grey zone consisted of urban or semi-urban areas not downwind of the four major industrial plants, which included up to one small factory. Finally, green zone included only rural or semi-rural areas without any industrial facility and not downwind of any industrial activity. Municipalities were considered as either rural/semi-rural or urban/semi-urban according to the Italian Central Statistical Institute official classification system (ISTAT 1986). Direct assessment of environmental exposure was hampered by the lack of information of air pollutant concentrations in the period preceding our investigation. Moreover, few monitoring stations are currently located in SP and only one in the green zone (town n. 4, Figure 1). A detailed description of air pollutant concentrations in SP for the period 1997–2010 has been reported in a previous paper (Parodi et al. 2014). Original raw data (hourly mean concentrations per day) are available at the Regional Environmental Protection Agency website (ARPAL 2005). Statistical analysis Association between environmental exposure and leukaemia risk was evaluated by binomial logistic regression model (Hosmer & Lemeshow 2000). Adjusted ORs were estimated including in the models the following putative confounders: type of interview, gender, age, smoking habit, educational level and exposure to radiation, pesticides or aromatic hydrocarbons. In more detail, type of interview was coded as personal, when the subject was personally interviewed, and as not personal if a relative answered the questionnaire. Age was included in the model as a continuous variable, using both a linear and a quadratic term. Exposure to tobacco smoking was categorized into three
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groups (ever, former and never smokers, respectively), while dose–response relationship was assessed including the average number of pack-years as nested variables, according to the method proposed by Leffondré et al. (2002). Educational level was considered as low for subjects with no diploma or only a primary school diploma, intermediate for those who obtained a lower class diploma and high for persons with high school diploma or university degree. Exposure to pesticides, hydrocarbons and radiations were included into the model as binary indicators (present/absent). Professional exposure to pesticide or hydrocarbons was assessed using the ISTAT-ATECO codes (ISTAT 1991) and an individual was considered as exposed if she/he performed a job in an exposed category for at least five years during her/his life. Finally, radiation exposure included professional, therapeutic and diagnostic ones; for these latter, only heavy radiations (namely, computerized axial tomography, scintigraphy and radioscopy) were taken into consideration. Simple logistic regression (Model I) and two multiple logistic regression models that provided ORs adjusted either for age and gender (Model II) or for all considered confounders (Model III) were fitted and compared. An analysis by cumulative exposure, defined as the length of residence in the most polluted area (red zone), was also carried out. All statistical tests were two sided and a p value lower than 0.05 was considered statistically significant. All analyses were performed by the statistical package Stata (version 11.1, Stata Corporation, College Station, TX).
Results Table 2 reports recent measures (period 2011–2012) of air pollution in SP. PM10 concentrations tended to be higher in the red zone than in the other two areas, except than in town n. 23 which corresponds to the dismantled factory. The pattern of PM2.5 levels was consistent with that of PM10, but no data for the green zone were available. Benzene concentrations were registered in three monitoring stations and were slightly higher in the red and grey zones. SO2 showed a heterogeneous pattern, while NO2 was very high in two stations in the red zone (towns n. 64 and n. 18) which are downwind to two big factories, a power plant and a coke oven, respectively, but not in the three remaining locations. Missing data for PM10 and PM2.5 were above 5 % for any considered station, except that in municipalities n. 2 and n. 18, in which they were about 10 %; missing data for benzene ranged from 3 and 7 %, for SO2 between 3 and 13 % and for NO2 between 1 and 9 %. With regard to previous measures of air pollution, data about benzene, NO2 and SO2 concentrations in the green zone, available since 2006 only, showed a pattern consistent with that reported in Table 2 (Parodi et al. 2014). In the area under study, 755 subjects were originally recruited. Among them, 5 (2 cases and 3 controls) were excluded because of not actually residing in the SP; 11 (2 cases and 9 controls) because they had been residing for less than 5 years; and 61 (6 cases and 55 controls) because their residence was not successfully located. Among the 678 eligible (237 leukaemia cases and 441 controls), 443 (164 cases and 279 controls) entered the study, while 235 were not interviewed for the following reasons: 40 (18 cases and 22 controls) died before the interview and no living relative was traced, 12 (4 cases and 8 controls) were affected by some physic or psychic impairments, 32 (23 cases and 9 controls) died before the interview and their relatives refused to participate in the study and, finally, 151 (28 cases and 123 controls) refused the interview.
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Table 2. Mean annual concentrations and standard deviations (in brackets) of some selected air pollutants measured in the SP in 2011–2012. Approximate location of monitoring stations is reported in Figure 1. Air pollutants (μg/m3) 2011 PM10
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Red zone Quiliano (n. 52)
24.6* (2.6) Vado L. (n. 64) 27.8* (3.5) Cairo M. (n. 15) 36.0* (12.2) Carcare (n. 18) 22.1* (12.3) Cengio (n. 23) 16.9* (3.5) Grey zone Savona (n. 56) Savona (n. 56) Albenga (n. 2) Green zone Albisola S. (n. 4)
14.1 (4.5) 21.4* (2.0) 21.5* (0.98) 15.3* (3.5)
PM2.5
2012
C6H6
SO2
17.0* n.a. (2.8) 18.9* 3.0 (4.3) (0.71) 23.8* n.a. (10.4) n.a. n.a.
n.a.
n.a.
n.a.
n.a.
2.9 (0.54) 14.4* n.a. (2.4) 14.9* n.a. (1.5) n.a.
1.4 (0.22)
NO2
PM10 PM2.5
19.7 34.4 (5.5) (11.2) 8.3 36.4 27.6 (1.3) (11.1) (5.1) n.a. 27.4 30.7 (9.0) (9.4) 23.9 46.4 27.4 (10.6) (19.8) (6.8) n.a. 15.4 11.1 (9.1) (5.0) n.a. 10.2 (2.4) 6 (1.6)
24.6 (6.7) 25.2 (4.4) 32.1 (5.3)
14.7 (3.4) 20.2 (3.7) 20.7 (3.5)
n.a.
n.a.
12.1 (3.4)
21.3 (6.5) 17.7 (4.3) 22.0 (7.4) n.a. n.a.
n.a. 13.4 (3.5) 14.2 (3.1) n.a.
C6H6
SO2
n.a.
n.a.
2.9 (0.81) n.a.
n.a.
NO2
18.7 (3.5) 2.8 5.9 36.6 (0.80) (1.3) (9.2) n.a. n.a. 21.1 (−7.0) n.a. 9.3 49.9 (3.6) (6.3) n.a. n.a. 17.4 (4.9)
n.a.
1.3 (0.29)
6.7 (6.0) 4.6 (1.0)
38.6 (9.5) 21.9 (3.4) 28.4 (6.1)
n.a.
n.a.
C6H6 = benzene; n.a. = not available; n = number of the host town reported in the legend of Figure 1. *Data available since 1 March 2011.
The proportion of interviewed subjects did not differ appreciably by zone of exposure (green: 61.1 %; grey: 59.3 %; red: 61.2 %; p = 0.885). No statistically significant difference was observed in the participation rate after stratification by case/control status (p = 0.212 among cases and p = 0.782 among controls, respectively). All potential confounders were similarly distributed among the two groups (Table 3), except education (18.3 % of cases had a high school diploma or university degree vs. 22.9 % of controls), even if statistical significance was not reached (p = 0.174, χ2 test). Moreover, controls were more likely to be personally interviewed (83.5 % vs. 50.6 %, p < 0.001). Lymphoblastic leukaemia was the most common type (n = 63), and included 7 acute, 49 chronic and 7 unspecified leukaemia. Myelocytic leukaemia included 58 cases (27 acute, 11 chronic and 20 unspecified). Finally, among the 43 other leukaemia, 2 cases were monocytic, 14 chronic erythremia, 9 megakaryocytic, 8 of unspecified cell type and 10 of uncertain behaviour. The small sample size prevented from performing subgroup analyses. Table 4 shows the estimates of association between residential exposure and risk of leukaemia. An excess risk was observed for the most polluted areas (OR = 1.11 for the grey zone and OR = 1.56 for the red zone compared to the green one, Model III), but statistical significance was not reached (p = 0.190). No trend in risk emerged from
400 Table 3.
S. Parodi et al. Characteristics of the 443 interviewed subjects by case-control status.
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Subject characteristics Age at recruitment Median age (IQR) Gender, N (%) Females Males Educational level, N (%) Low Intermediate High Tobacco smoking, N (%)* Never smokers Former smokers Current smokers Radiation exposure, N (%) Yes Not Pesticide exposure, N (%) Yes Not Aromatic hydrocarbon exposure, N (%) Yes Not Interview type, N (%) Subject Relative
Cases (164)
Controls (279)
72 (63–78)
71 (63–78)
72 (43.9) 92 (56.1)
120 (43.0) 159 (57.0)
92 (56.1) 42 (25.6) 30 (18.3)
131 (47.0) 84 (30.1) 64 (22.9)
92 (56.4) 49 (30.1) 22 (13.5)
153 (55.4) 73 (26.5) 50 (18.1)
33 (20.1) 131 (79.9)
59 (21.1) 220 (78.9)
18 (11.0) 146 (89.0)
37 (13.3) 242 (86.7)
42 (25.6) 122 (74.4)
56 (20.1) 223 (79.9)
83 (50.6) 81 (49.4)
233 (83.5) 46 (16.5)
p 0.942 0.855 0.174
0.400
0.797 0.481 0.175 30 years, compared with never resident, p = 0.435). Finally, when the length of residence was modelled as a continuous variable, no association was observed (p = 0.311, results not shown in Table 4). Discussion We found a small excess risk for residents in a heavily polluted zone of SP. Even if statistical significance was not reached, our estimates are consistent with those reported in a larger investigation in an area of southern Taiwan, Republic of China (Yu et al. 2006), that found a positive association between the exposure to industrial air pollution from some refineries, measured by an “opportunity” score, which corresponded to an OR = 1.5 for a residence of about three months at 1 km downwind of a petrochemical plant. However, in the present study, an effect of cumulative exposure was not found. This might be due to a lack of statistical power as a consequence of the small sample size of the exposed population. In fact, only 53 cases had resided in the red zone for at least one year during their life and, among them, only 15 were exposed for a period lower than 30 years, thus preventing a reliable analysis of dose–response trend. Apart from the smaller sample size, another difference between our investigation and that by Yu et al. (2006) is the higher heterogeneity of the sources of air pollutants in SP. Among them, the four big facilities included a fossil fuel power plant, which is a recognized source of
International Journal of Environmental Health Research Table 4. trend.
OR estimates by exposed areas, their related 95 % confidence intervals and test for
Exposed areas
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401
Exposure
Comparison by areas Model I Green zone Low Grey zone Mild Red zone High Model II Green zone Low Grey zone Mild Red zone High Model III* Green zone Low Grey zone Mild Red zone High Cumulative exposure Model I Red zone Never resident Red zone ≤ 30 years Red zone > 30 years Model II Red zone Never resident Red zone ≤ 30 years Red zone > 30 years Model III* Red zone Never resident Red zone ≤ 30 years Red zone > 30 years
Cases
Controls
OR
95 % CI
18 102 44
40 182 57
1 1.25 1.72
Ref 0.68–2.28 0.87–3.39
18 102 44
40 182 57
1 1.22 1.69
Ref 0.67–2.26 0.85–3.36
18 101 44
39 181 56
1 1.11 1.56
Ref 0.56–2.22 0.72–3.38
111 15 38
205 19 55
1 1.46 1.30
(ref) 0.71–2.98 0.79–2.05
111 15 38
205 19 55
1 1.42 1.29
(ref) 0.69–2.93 0.80–2.07
110 15 38
203 19 54
1 1.28 1.21
(ref) 0.58–2.82 0.70–2.08
p 0.093
0.099
0.190
0.253
0.241 0.435
Model I: unadjusted OR; Model II: OR adjusted for age and gender; Model III: OR adjusted for any considered confounder (namely, type of interview, gender, age, smoking habit, educational level, and exposure to radiation, pesticides or aromatic hydrocarbons). p = likelihood ratio test for trend. *4 missing data for smoking intensity.
carcinogenic air contaminants (Cohen 2000). Fumes were dispersed through two very tall stacks and their actual impact on resident population is not well assessed. The few measures of air pollutant concentrations in the municipality immediately downwind this facility (n. 64) provided a not fully consistent picture (Table 2), showing PM10 levels slightly higher than those observed in the grey zone, similar benzene concentrations, but very high NO2 levels. The value of SO2 concentration, which is a well-known index of industrial activity, was only slightly higher than that observed in the two stations of Savona municipality (n. 56) in the grey zone. Unfortunately, SO2 concentration in Quiliano (municipality n. 52), where the power plant is located, was not available. Furthermore, Savona town includes a rather large harbour that might have been an uncontrolled source of SO2 from shipping activities (ISPRA 2009). Further studies, adopting advanced simulation models of air pollutants concentrations, are needed in order to provide a more reliable assessment of industrial exposure to carcinogenetic compounds for the population residing in SP, especially in the areas surrounding the big power plant. With regard to the remaining three big factories, they included a coke oven, which represents a well-known source of many carcinogenic compounds at intensive local impact (Parodi et al. 2005; Tsai et al. 2008), a facility for the production of photographic films and equipment (located in the same municipality), and a big
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S. Parodi et al.
chemical industry dismantled in 1999, which produced a large variety of organic chemicals responsible of severe contamination of air, water and soil (Senato della Repubblica Italiana 1992; Esposito et al. 2002; Marengo et al. 2002; Semenzin et al. 2009, 2008). In particular, coke oven is a relevant source of benzene, but measures of this pollutant were not available. However, in both considered years, PM10, which represents another characteristic pollutant from coke oven activity (Cohen 2000; Parodi et al. 2005), was higher in the municipality were the facility was located (n. 15) than in the three monitoring stations located in the grey zone. Furthermore, the very high NO2 concentration in municipality n. 18, which is downwind to both the coke oven and a big chemical industry and also included a glasswork activity, is consistent with our knowledge about the kind of pollution from such industrial activities (Table 1). Finally, both PM10 and benzene concentrations were lower in the green zone than in the other two areas in both years. In spite of the lack of measures of air pollutant concentrations in most SP areas in past years (Parodi et al. 2014) and the observed heterogeneity of measures of pollutant concentrations, especially SO2, the few recent measures at our disposal (especially particulate matter and benzene) seems consistent with our definition of exposed zones. However, the lack of measures of personal exposure remains a major limit of our investigation, and some other uncontrolled sources of pollution might have biased our estimates. Among them, environmental exposure to pesticides from agricultural activity, which is more likely in the areas not exposed to industrial air pollution (i.e. all areas in the green zone and many areas in the grey zone), might have reduced the estimates of ORs by inducing a bias toward null. Moreover, leukaemia subtypes represent different malignant entities with a partially recognized different aetiology (Linet et al. 2006). As a consequence, our results, based on a pooled analysis, might be prone to some misclassification bias. However, industrial air pollution include a large variety of potential carcinogenic compounds and some previous investigations point out to an excess risk for all leukaemia types in environmentally exposed populations (Yu et al. 2006; Wong et al. 2009; García-Pérez et al. 2010). Cases and controls strongly differed by the proportion of the personal vs. proxy interview. Nevertheless, no relevant differences were observed comparing crude ORs with those adjusted by putative confounders, which included the type of interview. This result indicates that information about residential exposure did not differ between directly interviewed persons and their relatives, thus suggesting that residential history is not prone to severe recall bias. Moreover, confounders were distributed in a quite homogeneous proportion among cases and controls. In conclusion, our results are partly consistent with previous investigations indicating a possible role of industrial air pollutants on the risk of developing leukaemia in adults. The lack of a clear dose–response trend, the low statistical power and the poor measures of air pollutant concentrations demand caution in interpreting our findings and call for a more in-depth epidemiological investigation. Acknowledgements This work was supported by a grant provided by the Savona Local Health Centre 2 (ASL2 Savonese). We are deeply indebted with Maria Antonietta Gioioso, Giovanni Olivieri, Giuliano Chiappa, Virna Frumento, Claudio Boffa and Margherita Costa (Savona Local Health Centre 2, ASL2 Savonese), who carried out the interviews; Maria Paola Briata (ASL2 Savonese), who coordinated the interviewers activity; Matteo Puntoni (Galliera Hospital of Genoa) for data management; and Robin Nesbitt (Institute of Public Health, University of Heidelberg) for language revision.
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