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An Italian population-based case-control study on the association between farming and cancer: are pesticides a plausible risk factor? a

b

c

a

d

Christian Salerno , Antonella Carcagnì , Sara Sacco , Lucio Antonio Palin , Kris Vanhaecht , a

Massimiliano Panella & Davide Guido

b

a

Department of Translational Medicine. University of Eastern Piedmont “Amedeo Avogadro”. Via Solaroli n. 17-28100 Novara, Italy. b

Department of Brain and Behavioural Sciences. Section of Biostatistics, Neurophysiology and Psychiatry. Unit of Medical Statistics and Computational Genomics. Multivariate Statistics Laboratory. University of Pavia. Via Agostino Bassi n. 21-27110 Pavia, Italy.

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c

Department of Brain and Behavioural Sciences. Section of Biostatistics, Neurophysiology and Psychiatry. Unit of Medical Statistics and Computational Genomics. Clinical Epidemiology and Health Planning Laboratory. University of Pavia. Via Agostino Bassi n. 21-7110 Pavia, Italy. d

Health Services Research Group, School of Public Health, Faculty of Medicine, KU Leuven. University of Leuven. Kapucijnenvoer 35 blok d-box 7001-3000 Leuven, Belgium. Accepted author version posted online: 05 May 2015.

To cite this article: Christian Salerno, Antonella Carcagnì, Sara Sacco, Lucio Antonio Palin, Kris Vanhaecht, Massimiliano Panella & Davide Guido (2015): An Italian population-based case-control study on the association between farming and cancer: are pesticides a plausible risk factor?, Archives of Environmental & Occupational Health, DOI: 10.1080/19338244.2015.1027808 To link to this article: http://dx.doi.org/10.1080/19338244.2015.1027808

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ACCEPTED MANUSCRIPT An Italian population-based case-control study on the association between farming and cancer: are pesticides a plausible risk factor? Christian Salerno1*, Antonella Carcagnì2*, Sara Sacco3, Lucio Antonio Palin1, Kris Vanhaecht4, Massimiliano Panella1, Davide Guido2

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* = the first two Authors provided equal contributions.

1

= Department of Translational Medicine. University of Eastern Piedmont “Amedeo Avogadro”.

Via Solaroli n. 17 – 28100 Novara, Italy. 2

= Department of Brain and Behavioural Sciences. Section of Biostatistics, Neurophysiology

and Psychiatry. Unit of Medical Statistics and Computational Genomics. Multivariate Statistics Laboratory. University of Pavia. Via Agostino Bassi n. 21 – 27110 Pavia, Italy. 3

= Department of Brain and Behavioural Sciences. Section of Biostatistics, Neurophysiology

and Psychiatry. Unit of Medical Statistics and Computational Genomics. Clinical Epidemiology and Health Planning Laboratory. University of Pavia. Via Agostino Bassi n. 21 – 27110 Pavia, Italy. 4

= Health Services Research Group, School of Public Health, Faculty of Medicine, KU Leuven.

University of Leuven. Kapucijnenvoer 35 blok d - box 7001 – 3000 Leuven, Belgium.

Corresponding Author: Dr. Sara Sacco, M.D., Ph.D. student. Address: University of Pavia. Department of Brain and Behavioural Sciences. Section of Biostatistics, Neurophysiology and Psychiatry. Unit of Medical Statistics and Computational Genomics. Clinical Epidemiology and 1

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ACCEPTED MANUSCRIPT Health Planning Laboratory. Via Agostino Bassi n. 21 − 27100 Pavia, ITALY. Telephone number:

+39

0382987941.

Fax

number:

+39

0382987570.

E-mail

address:

[email protected]

Authors’ telephone numbers and e-mail addresses:

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1) Christian Salerno: +39 0321660653; [email protected]

2) Antonella Carcagnì: +39 0382987993; [email protected]

3) Sara Sacco: +39 0382987941; [email protected]

4) Lucio Antonio Palin: +39 0321660653; [email protected]

5) Kris Vanhaecht: +32 16336991; [email protected]

6) Massimiliano Panella: +39 0321660635; [email protected]

7) Davide Guido: +39 0382987993; [email protected]

Sources of Funding: None. Conflicts of Interest: The Authors declare that they have no conflict of interest.

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ACCEPTED MANUSCRIPT Abstract

This population-based case-control study investigated the association between farming (a proxy for pesticide exposure) and cancer in the Vercelli suburban area (NW Italy). The residents, aged 25 to 79 years, in the above-mentioned area during the period 2002-2009 were considered. Cases were all the first hospital admissions for cancer. Controls were all the

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subjects not included in the cases and not excluded from the study. Cases and controls were classified according to whether they occupationally resulted farmers or non-farmers during the period 1965-2009. Cancer odds ratios (ORs) between farmers and non-farmers were calculated with Generalized Linear Mixed Models adjusted by gender and age. Farmers showed a higher odds for all cancers (OR = 1.459, P < 0.001), non-melanoma skin cancer, colorectal and breast cancer. Results suggest a plausible association between pesticide exposure and cancer occurrence.

Key words: farmers; pesticides; cancer; Province of Vercelli; Italy. Word count for the abstract: 135 words. Word count for the main text: 3,207 words.

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Introduction

The Vercelli rice district (NW Italy, Piedmont Region, Province of Vercelli, 45°19′00″N / 8°25′00″E), extending over a cultivated area of 68,000 ha [1], is the most important Italian area for rice cultivation [2]. In 2007, 70.20% of the arable land in the Province of Vercelli was used

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to cultivate rice and 29.80% was used for other crops (20.89% other cereals, especially maize, and 8.91% other minor crops, i.e. forage, fruits and vegetables) [3]. This area plays a key role in the Italian agri-food industry, representing 33% of the national rice production in a country which accounts for more than 50% of the European Union rice production and exports roughly 70% [1]. The Vercelli rice district is known as one of the most technologically advanced rice cultivation areas in the world, due to the particularly intense mechanization of farms in field operations, including the maintenance of watering canals, bank management, ploughing, fertilising, harrowing, sowing, application of plant protection products and harvesting [1]. However, although wealth and jobs are created, rice production is said to be responsible for environmental impacts that are increasingly being perceived as topical [1]. Paddy fields (irrigated or flooded land used for growing rice) are in fact claimed to be responsible for soil and water pollution [1], due to the particular conditions of use of pesticides in this aquatic environment [4-7]. Pesticides used in Italian rice industry include 26 molecules, belonging to several classes of herbicides: Amides (propanil), Aryloxy phenoxy propionate (cyhalofop-butyl), Chlorinated acids (TCA), Chloroacetamide (pretilachlor), Cyclohexanedione (cycloxydim), Oxadiazoles (oxadiazon), Phenoxy acids (2,4-D, 2,4-DB, 2,4-DP, MCPA, MCPP), Picolinic

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(triclopyr),

Thiocarbamates

(dimepiperate,

molinate,

thiobencarb,

tiocarbazil),

Sulfonylureas (azimsulfuron, bensulfuron-methyl, cinosulfuron, ethoxysulfuron, metosulam, metsulfuron-methyl), Quinolines (quinclorac), bentazon, flurenol and pyrazoxyifen [4]. A recent research by the Italian Higher Institute for the Environmental Protection and Research (Istituto Superiore per la Protezione e la Ricerca Ambientale, ISPRA) found 40 pesticides in Piedmont water: 13.9% of surface water samples and 9.0% of groundwater ones result to be polluted by

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pesticides whose quantity exceeds law limits for drinking water [8]. The most frequently found compounds are terbuthylazine, metolachlor, terbuthylazine-desethyl, oxadiazon, bentazon, triciclazol, MCPA, azoxystrobin, hexachlorobenzene in surface water samples, terbuthylazinedesethyl, atrazine-desethyl, terbuthylazine, atrazine, oxadiazon, bentazon, metolachlor, 2,6dichlorobenzamide, dimethenamid in groundwater samples [8]. According to the Regional Agency for Environmental Protection (Agenzia Regionale per la Protezione dell’Ambiente, ARPA) of Piedmont Region, 74 out of 87 groundwater monitoring points in the Vercelli area show a contamination by pesticides: the most frequently found compounds are bentazon (17,39%), atrazine (15,31%), hexazinone (14,67%), oxadiazon (14,39%), nicosulfuron (12,85%), simazine (12,12%), terbuthylazine-desethyl (11,43%) e terbuthylazine (10,05%) [9]. Pesticides now out of production (e.g. atrazine, forbidden since the early 90’s) are still detectable in environmental matrices, because of their strong use in the past and their high environmental persistence [10]. Agricultural pesticides are extremely harmful to environmental and human health and to several very useful living organisms [5,7,11]. Several pesticides are associated with various human cancers, such as lung, breast and pancreatic cancer, non-Hodgkin lymphoma, leukaemia, brain,

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ACCEPTED MANUSCRIPT prostate, stomach, ovarian and kidney cancer [12-18]. Among the above-mentioned 26 molecules of herbicides used in Italian rice industry, Phenoxy acids report associations with non-Hodgkin lymphoma and sarcoma in some human studies [13,19] and Thiocarbamates might be considered possible carcinogens [20-23]. Farmers have numerous opportunities for exposure to pesticides (e.g. during planting and cultivation of crops, pesticide application to crops, storage areas, mixing and preparing pesticides

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for application, loading and cleaning application equipment) and may be at greater risk for pesticide exposure if they incorrectly handle, store or dispose of pesticides or if they do not wear personal protective gear [24]. Farmers may therefore be at higher risk for acute and chronic effects associated with pesticides [24]. Literature shows a well-known excess of cancer morbidity and mortality in the Province of Vercelli [25-30], but only few studies focus on cancer in local farmers and on the possible role of their professional exposure to pesticides [31]. Studying farmers could allow to assess the importance of professional exposure factors in explaining cancer morbidity and mortality excesses in the above-mentioned area. The present study aimed to investigate the association between farming (considered a proxy for pesticide exposure) and cancer in the suburban area of Vercelli.

Methods

Study design The Province of Vercelli consists of 86 Municipalities. In the present population-based casecontrol study [32], the suburban area of Vercelli was considered: it is a subset of the Province of

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ACCEPTED MANUSCRIPT Vercelli consisting of 18 Municipalities (see Figure 1). The Authors chose this area because its arable land is almost entirely used for rice cultivation. The northern part of the Province of Vercelli is mountainous and not used for rice production. All the residents living at least for one year during the 2002-2009 time period in the abovementioned suburban area of Vercelli and having a range of age from 25 to 79 years were considered. Data were taken from the register offices of the 18 Municipalities forming the

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suburban area of Vercelli. Cases were defined as all the first hospital admissions for cancer (new cancer cases) recorded in the above-mentioned subjects. The Province of Vercelli is not served by a cancer registry [26]. Therefore, to identify new cancer cases, included extra Regional passive mobility, the union of the following data sources was used: hospital discharge forms (Schede di Dimissione Ospedaliera, SDOs) from 2000 to 2009; Italian National Institute of Statistics (Istituto Nazionale di Statistica, ISTAT) death forms. A subsequent cross-check with histopathology reports, available from 1998, was done: new cancer cases not confirmed by histopathology reports were excluded. Consulting SDOs concerning the 2000-2001 time period and histopathology reports from 1998 to 2001 allowed to identify and exclude prevalent cancer cases (admissions after the first hospitalization). SDOs and ISTAT death forms were consulted in the archive of the Local Health Unit of Vercelli [33]. Histopathology reports were taken from the St. Andrew Hospital of Vercelli [34] and from the main Italian centres of research and treatment of cancer (i.e. the National Institute of Tumours and the European Institute of Oncology) towards which there is a strong patient mobility [35, 36].

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ACCEPTED MANUSCRIPT Controls were defined as all the residents living at least for one year during the 2002-2009 time period in the suburban area of Vercelli, having a range of age from 25 to 79 years, not included in the cases and not excluded from the study as prevalent cancer cases or new cancer cases not confirmed by histopathology reports. The set of cases and controls formed the reference population of the present population-based case-control study.

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Cases and controls were then classified according to whether they occupationally resulted farmers or non-farmers. This classification was made by consulting (by mean of birth date and fiscal code) data about social security contributions paid during the 1965-2009 time period: only subjects who paid at least one year of social security contributions as farmers were considered as such. All the others (included those who never worked) were considered as non-farmers. Data about social security contributions were taken from Italian National Institute of Social Security (Istituto Nazionale di Previdenza Sociale, INPS) database [37]. Farming was considered a proxy for pesticide exposure [38]. Taking into account only cancer cases from 25 to 79 years old resulted in some practical implications: on the one hand paediatric and youth cases (in which professional exposure is not relevant) were not taken into consideration; on the other hand a 10-year latency period between exposure end and cancer appearance was considered (given the farmers' well-established habit to go on working over the age of 70) and cancer cases attributable to advanced age (80+) were excluded. The Authors decided to use new cancer cases instead of prevalent ones, in order to avoid profession redundancy and to be sure that every worker (and his profession) was counted only

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ACCEPTED MANUSCRIPT once. In the few cases of multiple cancer in the same person, profession was counted one single time. The flow diagram of the present study is reported in Figure 2.

Statistical analysis To evaluate the association between farming (1 = farmer, 0 = non-farmer) and cancer, a crude

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analysis was initially performed. At first, 2x2 tables reporting the number of cases vs. controls for all cancers (1 = case, 0 = control) and for each cancer site (1 = case in the considered cancer site, 0 = control or case in other cancer sites) recorded in farmers vs. non-farmers were constructed. Crude odds ratios (ORs) with their 95% confidence intervals (95% CI) and P-values were then calculated. The significance of the comparison between the two workers’ groups was evaluated with Fisher’s exact test. To refine the analysis, Generalized Linear Mixed Models (GLMMs) with canonical link logit were then performed [39]. Models were fitted for all cancers and for each cancer site (response variable). GLMMs were adjusted by gender and age (classified in 11 five-year classes of age); occupational risk factors related to farming were taken as explanatory variables; a hierarchical random effect on the Municipality of residence was also included in order to improve the fit of the model. The exponential transformation of the interesting parameters (i.e. those associated with the risk factor) allowed to obtain more easily interpretable ORs, on which 95% CI were calculated. The model parameters were tested with t-test. GLMMs were applied to those cancer sites that showed at least 10 cases. When the variability among the Municipalities (random effect) was equal to zero or very small, GLMMs reduced to a multiple logistic regression model.

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ACCEPTED MANUSCRIPT P-values < 0.01 were considered statistically significant. Such a low target significance level was chosen in order to reduce the likelihood of identifying a statistically significant association by chance. P-values < 0.001 were considered highly statistically significant and P-values between 0.01 and 0.05 were considered to be indicative of a suspect statistical significance. Calculations were carried out using the statistical software R, version 2.15.3 [40], and its

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package lme4 [41].

Results

Reference population The reference population (i.e. the set of cases and controls) amounted to 12,378 residents (6,078 males and 6,300 females): there were 887 cases and 11,491 controls. Farmers were 1,481 (719 males and 762 females): there were 241 farmers among the cases and 1,240 farmers among the controls. The Municipalities showing the highest proportions of farmers were Collobiano (186 farmers per 1,000 residents), Olcenengo (172 farmers per 1,000 residents) and Albano Vercellese (162 farmers per 1,000 residents), all located in the Northern zone of the Vercelli suburban area (see Figure 1). Reference population, classified by Municipality, gender and presence of farmers, is described in Table 1.

Main findings

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ACCEPTED MANUSCRIPT Cases recorded in the reference population by classes of age are reported in Table 2. Increasing age was accompanied by an increase in cancer relative frequency (per 1,000 residents): the association between age and cancer, although spurious, was thus confirmed. The age classes with the highest number of cases ranged from 55 to 79 years: the majority of cases was in the older age class, ranging from 75 to 79 years (200 cases, 166.11 cases per 1,000 residents). Table 3 shows, for each cancer site, the number of cases in the whole reference population

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(absolute frequencies and relative frequencies per 1,000 residents), in farmers (absolute frequencies and relative frequencies per 1,000 farmers) and in non-farmers (absolute frequencies and relative frequencies per 1,000 non-farmers), with respective crude ORs, 95% CI and Pvalues. A total of 887 cases (71.66 cases per 1,000 residents) was reported: 241 in farmers (162.73 cases per 1,000 farmers) and 646 in non-farmers (59.28 per 1,000 non-farmers). As regards all cancers, crude OR was equal to 3.084 (95% CI = 2.618 – 3.624; P < 0.001). As regards the single most frequent cancer sites, highly significant results (P < 0.001) were reported for colon-rectum (crude OR = 3.389), skin non-melanoma (crude OR = 3.954), breast (crude OR = 2.545), prostate (crude OR = 4.050) and kidney (crude OR = 3.874). The present study found one case of breast cancer in a male: it is worth mentioning for its extreme rarity. Significant results (P < 0.01) were reported for oral cavity (crude OR = 6.147) and lung (crude OR = 2.165). Suspect evidences (0.01 ≤ P < 0.05) were reported for liver + biliary system (crude OR = 2.684), skin melanoma (crude OR = 2.582), ovary (crude OR = 4.097), nervous system (crude OR = 4.025) and stomach (crude OR = 3.687). Table 3 also reports, only for descriptive purposes, cancer sites showing a low frequency of cases in the considered reference population: endocrine system, thymus, peritoneum, mesothelium,

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ACCEPTED MANUSCRIPT testicle, vulva, bones, pharynx, larynx, oesophagus, small intestine and penis. No cases of leukaemia occurred. The refined and adjusted analysis with GLMMs (see Table 3 and Figure 3) showed that all cancers considered together were significantly and positively associated with farming: cancer odds was 1.459 times higher in farmers than in non-farmers of the same gender and age class with highly statistical significance (OR = 1.459, P-value < 0.001). In detail, farming showed a

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positive and statistically significant association (P < 0.01) with non-melanoma skin cancer (OR = 1.844, P-value = 0.004). Suspect evidence (0.01 ≤ P < 0.05) of positive association resulted for colorectal cancer (OR = 1.529, P-value = 0.044) and breast cancer (OR = 1.720, P-value = 0.035).

Discussion

Main findings The present population-based case-control study highlighted that, in the considered reference population, farmers compared to non-farmers showed a higher odds for all cancers. This epidemiological excess could confirm the well-known association between pesticide exposure and cancer occurrence. Pesticides used, in the past and today, for rice cultivation contain a great mixture and variety of active molecules, with different tropism for body organs [8, 9]: these substances may have contributed to the excess of all cancers in farmers. This excess, however, could also be due to several factors to which farmers are more exposed than the general population (e.g. solar radiation), to farmers’ cultural reluctance to prevention, to the difficulty in

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ACCEPTED MANUSCRIPT raising their awareness on the use of personal protective equipment and to the absence of an occupational physician who periodically check farmers’ health. Finally, a past exposure to agricultural pesticides since childhood or even in utero, with silent genetic damage at birth, cannot be excluded. Pregnancy, breast-feeding, fetal life, childhood and puberty are key periods in which exposure to such substances can cause serious damages: much more attention must be paid since such effects can be transgenerational [7, 42].

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In the reference population, farmers compared to non-farmers showed a significantly higher odds for non-melanoma skin cancer. Suspect evidences of positive associations were found for breast cancer and colorectal cancer. As far as single tumour excesses are concerned, there are many hypothesis supported by scientific studies. For instance, for non-melanoma skin cancer, the most common risk factors are exposure to ultraviolet rays (continuous exposure to sunlight and sunburn may increase the risk) and very fair skin, i.e. skin phototype 1 and 2 [43, 44]. Among pesticides, inorganic arsenic compounds are mentioned as risk factors [45]. As regards tumours involving specific hormones such as breast cancer, it is possible to mention a specific pesticide category named “endocrin disruptors” [7, 42]. For example it has been recently shown that exposure to DichloroDiphenylTrichloroethane (DDT, an insecticide used in the 50s, prohibited many years ago, but still present in environmental matrices) is linked to a higher risk of breast cancer if the exposure takes place in prepubertal age [7]. It is also worth mentioning the possible dioxin contamination of 2,4-D [46]. This phenoxy herbicide, at present used in the Vercelli rice district, was in the past frequently co-formulated with the herbicide 2,4,5-T (banned since 1975) [4, 46]. Production of 2,4,5-T was contaminated with the carcinogenic dioxin TCDD [46]. Those who were exposed to the mixed formulations might therefore have been exposed to TCDD and

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ACCEPTED MANUSCRIPT this could represent a risk factor for breast cancer occurrence [46]. Concerning colorectal cancer, the possible epidemiological explanation is more difficult. It is well-known that genetic factors, diet and chronic inflammatory bowel diseases are important risk factors for this type of cancer. The available evidence suggests a limited role of environmental and occupational exposures in its aetiology and increased risks among farmers have only rarely been observed [47].

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Study limitations The use of hospital based cases was a limitation for possible errors in drafting the SDOs modules. However, the subsequent cross-check with histopathology reports guaranteed the reliable identification of cases. Excluding prevalent cancer cases (i.e. admissions after the first hospitalization) could also represent a study limitation, but the Authors preferred to use only new cancer cases, in order to avoid profession redundancy and to be sure that every worker (and his profession) was counted only once. The lack of information about the products cultivated by the farmers may represent a selection bias. However, the arable land in the suburban area of Vercelli is almost entirely used for rice cultivation. Therefore, it could be assumed that almost all the farmers in the area were involved in rice growing. Furthermore, no major confounders were considered. The present populationbased case-control study didn’t take in account genetic, socio-demographic and behavioural characteristics of the two considered workers’ groups (e.g. place of birth, place of residence, income, voluptuary habits, etc.). Some of them (e.g. place of residence close to health-risky sites, tobacco smoking, alcohol drinking) could be responsible for part of the observed cancer excesses and therefore may be deepened in further studies.

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ACCEPTED MANUSCRIPT The statistical analysis does not really account for the very large number of control subjects. Therefore, the Authors felt appropriate to choose a low target significance level (P < 0.01). Finally, the exposure measurement used in the present study was very crude and likely had a large degree of misclassification. However, it is important to remember that this is the first inferential study realized with demonstrative intent in the Vercelli rice district. In subsequent studies, it could also be interesting to evaluate the job-exposure matrix of cases among farmers,

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in order to find out the specific pesticides they used during their employment history and to bring them back to the occurred cancer.

Conclusions The present population-based case-control study showed that farmers compared to non-farmers have a higher odds for several cancers, suggesting a plausible association between exposure to agricultural pesticides and cancer occurrence. The present results confirm what is already known from literature. However, it is important to remember that this is the first inferential study which addresses the issue in the Vercelli rice district, one of the most important rice cultivation areas in the world. Further studies are needed in order to examine in detail the issue. However, the priority is to immediately implement primary prevention. Pesticide exposure is in fact a problem involving not only some professional categories, but also the whole general population: the molecules of pesticides are now permanently entered in the ecosystem, contaminating waters, land, food and also appearing in cord blood and breast milk. Primary prevention in this field is the best strategy. The demonstration comes from Sweden: in the 70’s, the courageous research conducted by Hardell

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ACCEPTED MANUSCRIPT led to the prohibition of some agricultural pesticides; now, after thirty years, incidence of nonHodgkin lymphoma is decreasing [48]. Primary prevention is also suggested by an Italian survey, recommending policies to encourage organic agriculture, in order to significantly contain

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exposure to dangerous chemicals [49].

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References

1. Blengini GA, Busto M. The life cycle of rice: LCA of alternative agri-food chain management systems in Vercelli (Italy). J Environ Manage 2009; 90 (3): 1512–1522.

Downloaded by [OARE Consortium] at 03:35 03 July 2015

2. Fusi

A,

Bacenetti

J,

González-García

S,

Vercesi

A,

Bocchi

S,

Fiala

M.

Environmental profile of paddy rice cultivation with different straw management. Sci Total Environ 2014; 494–495: 119–128.

3. Piemonte in cifre 2007. Regional Statistic Yearbook. 11.11 Cultivated surface, yield and production of the principal agricultural products - Province of Vercelli Years 2004-2006. Available from: http://www.2007.piemonteincifre.it/set_b.html (Accessed 2015 Feb 17)

4. Ferrero A, Tabacchi M. L’ottimizzazione del diserbo nel riso. In: Montemurro P, Onofri A, editors. Il controllo della flora infestante: un esempio di ottimizzazione a vantaggio dell’ambiente e della produzione. Proceedings of the XII SIRFI (Società Italiana per la Ricerca sulla Flora Infestante) Biennial Conference; Milan, Italy, 2000 December 5-6: p. 111–150.

Available

from:

http://www.sirfi.it/en/download/doc_download/42-2000-

milano.html (Accessed 2014 March 25)

17

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ACCEPTED MANUSCRIPT 5. Mostafalou S, Abdollahi M. Pesticides and human chronic diseases: evidences, mechanisms, and perspectives. Toxicol Appl Pharmacol 2013; 268 (2): 157–177.

6. Sugeng AJ, Beamer PI, Lutz EA, Rosales CB. Hazard-ranking of agricultural pesticides for chronic health effects in Yuma County, Arizona. Sci Total Environ 2013; 463–464C: 35–41.

Downloaded by [OARE Consortium] at 03:35 03 July 2015

7. Gentilini P. Pesticidi, cancro e salute. Arezzo, Italy: Associazione Medici per l’Ambiente ISDE

Italia,

2009.

Available

from:

https://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&ved=0CDk QFjAC&url=http%3A%2F%2Fwww.chefare.eu%2Fdominio%2Fmenu-amministrazionemp%2Farea-download%2Fcategory%2F1-pesticidi.html%3Fdownload%3D10%3Apesticidicancro-esalute&ei=PfnJUdXfMIXVPMvDgdgI&usg=AFQjCNH_luEL8mWfAih1NDb2BBladOCo2 A (Accessed 2014 Mar 25)

8. ISPRA. Rapporto nazionale pesticidi nelle acque: dati 2011-2012. Edizione 2014. Tabelle regionali. Rapporti 208/2014. Roma, Italy: ISPRA – Settore Editoria, 2014. ISBN 978-88448-0681-1.

Available

from:

http://www.isprambiente.gov.it/it/pubblicazioni/rapporti/rapporto-nazionale-pesticidi-nelleacque.-dati-2011-2012.-edizione-2014 (Accessed 2015 Feb 17)

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ACCEPTED MANUSCRIPT 9. ARPA Piemonte. Progetto sull’inquinamento da fonti diffuse. Studio di alcune aree campione al fine di predisporre piani regionali di intervento. Parte II – Acque sotterranee. Relazione finale. Torino, Italy: ARPA Piemonte, 2005. Document code: SS02.06-D13/05. Available from: http://www.arpa.piemonte.it/approfondimenti/temi-ambientali/acqua/acquesotterranee/ProgettosullInquinamentodaFontidiffuse.pdf (Accessed 2014 March 25)

Downloaded by [OARE Consortium] at 03:35 03 July 2015

10. ISPRA. Rapporto nazionale pesticidi nelle acque: dati 2009-2010. Edizione 2013. Rapporti 175/2013. Roma, Italy: ISPRA – Settore Editoria, 2013. ISBN 978-88-448-0595-1. Available from: http://www.isprambiente.gov.it/it/pubblicazioni/rapporti/rapporto-nazionale-pesticidinelle-acque-dati-2009-2010.-edizione-2013 (Accessed 2014 March 25)

11. Girolami V, Mazzon L, Squartini A, Mori N, Marzaro M, Di Bernardo A, Greatti M, Giorio C, Tapparo A. Translocation of neonicotinoid insecticides from coated seeds to seedling guttation drops: a novel way of intoxication for bees. J Econ Entomol 2009; 102 (5): 1808– 1815.

12. Andreotti G, Hou L, Beane Freeman LE, Mahajan R, Koutros S, Coble J, Lubin J, Blair A, Hoppin JA, Alavanja M. Body mass index, agricultural pesticide use, and cancer incidence in the Agricultural Health Study group. Cancer Causes Control 2010; 21 (11): 1759–1775.

19

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT 13. Bassil KL, Vakil C, Sanborn M, Cole DC, Kaur JS, Kerr KJ. Cancer health effects of pesticides: systematic review. Can Fam Physician 2007; 53 (10): 1704–1711.

14. Blair A, Sandler DP, Tarone R, Lubin J, Thomas K, Hoppin JA, Samanic C, Coble J, Kamel F, Knott C, Dosemeci M, Zahm SH, Lynch CF, Rothman N, Alavanja MC. Mortality among

Downloaded by [OARE Consortium] at 03:35 03 July 2015

participants in the agricultural health study. Ann Epidemiol 2005; 15 (4): 279–285.

15. Bonner MR, Williams BA, Rusiecki JA, Blair A, Beane Freeman LE, Hoppin JA, Dosemeci M, Lubin J, Sandler DP, Alavanja MC. Occupational exposure to terbufos and the incidence of cancer in the Agricultural Health Study. Cancer Causes Control 2010; 21 (6): 871–877.

16. Hou L, Andreotti G, Baccarelli AA, Savage S, Hoppin JA, Sandler DP, Barker J, Zhu ZZ, Hoxha M, Dioni L, Zhang X, Koutros S, Freeman LE, Alavanja MC. Lifetime Pesticide Use and Telomere Shortening among Male Pesticide Applicators in the Agricultural Health Study. Environ Health Perspect 2013; 121 (8): 919–924.

17. Sanborn M, Cole D, Kerr K, Vakil C, Sanin LH, Bassil K. Pesticides literature review. Toronto (Ontario, Canada): The Ontario College of Family Physicians, 2004. Available from: http://www.ocfp.on.ca/docs/public-policy-documents/pesticides-literature-review.pdf (Accessed 2014 March 25)

20

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT 18. Wigle DT, Turner MC, Krewski D. A systematic review and meta-analysis of childhood leukemia and parental occupational pesticide exposure. Environ Health Perspect 2009; 117 (10): 1505–1513.

19. Pahwa M, Beane Freeman L, Spinelli JJ, Blair A, Pahwa P, Dosman JA, McLaughlin JR, Demers PA, Hoar Zahm S, Cantor KP, Weisenburger DD, Harris SA. 0409 The North

Downloaded by [OARE Consortium] at 03:35 03 July 2015

American Pooled Project (NAPP): Pooled analyses of case-control studies of pesticides and agricultural exposures, lymphohematopoietic cancers and sarcoma. Occup Environ Med 2014; 71 Suppl 1: A116.

20. Blair A, Ritz B, Wesseling C, Beane Freeman L. Pesticides and human health. Occup Environ Med 2015; 72 (2): 81-82.

21. Alavanja MC, Samanic C, Dosemeci M, Lubin J, Tarone R, Lynch CF, Knott C, Thomas K, HoppinJA,

Barker

J,

Coble

of agricultural pesticides and prostate

J,

Sandler

cancer risk in

DP,

Blair

A.

Use

the Agricultural Health Study cohort.

Am J Epidemiol 2003; 157 (9): 800-814.

22. Alavanja MC. Introduction: pesticides use and exposure extensive worldwide. Rev Environ Health 2009; 24 (4): 303-309.

21

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT 23. Alavanja

MC, Hofmann

JN, Lynch

CF, Hines

CJ, Barry

KH, Barker

J, Buckman

DW, Thomas K, Sandler DP, Hoppin JA, Koutros S, Andreotti G,Lubin JH, Blair A, Beane Freeman LE. Non-hodgkin lymphoma risk and insecticide, fungicide and fumigant use in the agriculture health study. PLoS One 2014; 9 (10): e109332.

24. Martin SA Jr, Sandler DP, Harlow SD, Shore DL, Rowland AS, Alavanja MC. Pesticide use

Downloaded by [OARE Consortium] at 03:35 03 July 2015

and pesticide-related symptoms among black farmers in the Agricultural Health Study. Am J Ind Med 2002; 41 (3): 202–209.

25. Salerno C, Berchialla P, Palin L, Panella M. Cancer survival in a local health district in Piemonte (Italy): follow up to 2007. Ig Sanita Pubbl 2013; 69 (1): 39–46.

26. Salerno C, Palin L, Comelli M, Panella M. An update on several indicators of the quality of data flow in a cancer registry in Vercelli (Piedmont, Italy). Ig Sanita Pubbl 2012; 68 (5): 697–706.

27. Salerno C, Comelli M, Palin L, Panella M. Cancer mortality in a local health district of Vercelli (Italy) 2000-2009. Ig Sanita Pubbl 2011; 67 (3): 281–91.

28. Salerno C, Bagnasco G, Trovato AM. Analysis of the incidence of tumours subjected to screening in a local health authority in Vercelli (Italy), 2002-2005: preliminary data. Ig Sanita Pubbl 2010; 66 (2): 229–235.

22

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT

29. Salerno C, Bagnasco G, Comelli M, Panella M. Epidemiology of tumour mortality from 2000 to 2005 in the province of Vercelli (Italy). Ig Sanita Pubbl 2009; 65 (5): 467–474.

30. Salerno C, Bagnasco G, Panella M, Comelli M. Estimating the incidence of cancer in the VC Local Health Authority in Vercelli (Italy) from 2002 to 2005. Ig Sanita Pubbl 2009; 65 (3):

Downloaded by [OARE Consortium] at 03:35 03 July 2015

253–260.

31. Salerno C, Comelli M, Pastena M, Mundo A, Leghini A, Bombelli S, Panella M, Bagnasco G. Comparison of the leukaemia incidence in two groups of farmers and traders in the Vercelli province of Italy, 2002-2009. Ann Ig 2011; 23 (1): 27–32.

32. Armenian HK, Khlat M. Occupational studies. In: Armenian HK. The Case-Control Method. Design and Applications. New York, NY, USA, 2009: Oxford University Press: pp. 212-213. ISBN 978-0-19-518711-3.

33. Azienda

Sanitaria

Locale

Vercelli.

Official

Website,

2014.

Available

from:

http://www.aslvc.piemonte.it/ (Accessed 2014 March 26)

34. Presidio Ospedaliero S. Andrea – Vercelli. Official Website, 2014. Available from: http://www.aslvc.piemonte.it/vercelli/o_presen.htm (Accessed 2014 March 26)

23

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT 35. Fondazione IRCCS – Istituto Nazionale dei Tumori. Official Website, 2014. Available from: http://www.istitutotumori.mi.it/ (Accessed 2014 March 26)

36. Istituto

Europeo

di

Oncologia.

Official

Website,

2014.

Available

from:

Downloaded by [OARE Consortium] at 03:35 03 July 2015

https://www.ieo.it/Italiano/Pages/Default.aspx (Accessed 2014 March 26)

37. INPS. Official Website, 2014. Available from: https://www.inps.it/portale/default.aspx (Accessed 2014 March 26)

38. Armenian HK. Cholera Epidemic in Lusaka, Zambia. In: Armenian HK. The Case-Control Method. Design and Applications. New York, NY, USA, 2009: Oxford University Press: p. 135. ISBN 978-0-19-518711-3.

39. Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal Analysis. Hoboken, NJ, USA: John Whiley & Sons, 2004.

40. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2013. Available from: http://www.R-project.org/ (Accessed 2014 March 25)

24

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT 41. Bates D, Maechler M, Bolker B, Walker S. lme4: Linear mixed-effects models using Eigen and

S4.

R

package

version

1.1-5,

2014.

Available

from:

http://CRAN.R-

project.org/package=lme4 (Accessed 2014 March 25)

42. Skinner MK. Endocrine disruptor and epigenetic transgenerational disease etiology. Pediatr

Downloaded by [OARE Consortium] at 03:35 03 July 2015

Res 2007; 61 (5 Pt 2): 48R–50R.

43. Corona R, Dogliotti E, D'Errico M, Sera F, Iavarone I, Baliva G, Chinni LM, Gobello T, Mazzanti

C, Puddu

P, Pasquini

P.

Risk factors for basal cell carcinoma in

a Mediterranean population: role of recreational sun exposure early in life. Arch Dermatol 2001; 137 (9): 1162–1168.

44. Rosso S, Zanetti R, Martinez C, Tormo MJ, Schraub S, Sancho-Garnier H, Franceschi S, Gafà L, Perea E, Navarro C, Laurent R, Schrameck C, Talamini R, Tumino R,Wechsler J. The multicentre south European study “Helios”. II: Different sun exposure patterns in the aetiology of basal celland squamous cell carcinomas of the skin. Br J Cancer 1996; 73 (11): 1447–1454.

45. Bonadonna G, Robustelli Della Cuna G, Valgussa P. Medicina oncologica. Milano, Italy: Elsevier Masson, 2007.

25

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT 46. Boffetta P, Mundt KA, Adami HO, Cole P, Mandel JS. TCDD and cancer: a critical review of epidemiologic studies. Crit Rev Toxicol 2011; 41 (7): 622-636.

47. Settimi L, Comba P, Bosia S, Ciapini C, Desideri E, Fedi A, Perazzo PL, Axelson O. Cancer risk among male farmers: a multi-site case-control study. Int J Occup Med Environ

Downloaded by [OARE Consortium] at 03:35 03 July 2015

Health 2001; 14 (4): 339–347.

48. Hardell L. Pesticides, soft-tissue sarcoma and non-Hodgkin lymphoma--historical aspects on the precautionary principle in cancer prevention. Acta Oncol 2008; 47 (3): 347–354.

49. Bartoli L, Bartoli V, Severo A. La mortalità italiana in agricoltura a confronto con industria e terziario.

Agriregionieuropa

2010;

anno

6

n.

23.

Available

from:

http://www.agriregionieuropa.univpm.it/content/article/31/23/la-mortalita-italianaagricoltura-confronto-con-industria-e-terziario (Accessed 2014 March 25).

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ACCEPTED MANUSCRIPT Table 1 Reference population: residents (absolute frequencies) and farmers (absolute frequencies and relative frequencies per 1,000 residents) by Municipality and gender.

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RESIDENTS Municipality

Total

Albano

297

Asigliano

1,183

Borgo Vercelli

1,724

Caresanablot

817

Collobiano

113

Costanzana

729

Desana

881

Lignana

481

Olcenengo

529

Oldenico

215

Pertengo

325

Pezzana

957

Prarolo

498

Quinto

363

Rive

345

Stroppiana Tricerro Villata

1,031 545 1,345

FARMERS (relative frequencies per 1,000 residents) Males Females Total Males Females 48 27 21 159 138 (162) (170) (132) 133 59 74 575 608 (112) (103) (122) 132 68 64 827 897 (77) (82) (71) 33 20 13 401 416 (40) (49) (31) 21 12 9 59 54 (186) (203) (166) 100 39 61 333 396 (137) (117) (154) 124 52 72 424 457 (140) (126) (157) 60 33 27 246 235 (124) (134) (114) 91 46 45 264 265 (172) (174) (169) 34 20 14 108 107 (158) (185) (130) 45 24 21 150 175 (138) (160) (120) 143 63 80 484 473 (149) (130) (169) 59 28 31 237 261 (118) (118) (119) 39 28 11 190 173 (107) (147) (63) 44 22 22 160 185 (127) (138) (118) 124 63 61 504 527 (120) (125) (115) 82 35 47 269 276 (150) (130) (170) 169 80 89 688 657 (125) (116) (135) 27

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1,481 (120)

6,300

719 (118)

762 (121)

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TOTAL

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Table 2

Reference population (absolute frequencies of residents) and cases (absolute

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frequencies and relative frequencies per 1,000 residents) by classes of age. Classes of age

RESIDENTS

25-29 years

891

30-34 years

1,059

35-39 years

1,199

40-44 years

1,174

45-49 years

1,022

50-54 years

1,110

55-59 years

1,037

60-64 years

1,136

65-69 years

1,243

70-74 years

1,303

75-79 years

1,204

TOTAL

12,378

CASES (relative frequencies per 1,000 residents) 5 (5.61) 9 (8.50) 20 (16.68) 34 (28.96) 39 (38.16) 46 (41.44) 106 (102.22) 94 (82.75) 157 (126.31) 177 (1,358.84) 200 (166.11) 887 (71.66)

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ACCEPTED MANUSCRIPT Table 3 Number of cases by cancer site in the reference population (absolute frequencies and relative frequencies per 1,000 residents), in farmers of the reference population (absolute frequencies and relative frequencies per 1,000 farmers) and in non-farmers of the reference population (absolute frequencies and relative frequencies per 1,000 nonfarmers): results of crude analysis (odds ratios -ORs- with 95% Confidence Intervals -CIsand P-values) and Generalized Linear Mixed Models (GLMMs) analysis (ORs with 95%

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CIs and P-values). CASES Cancer site

Oral cavity f

No. of cases in the reference population a (No. per 1,000 residents) 11 (0.9)

No. of cases in farmers b (No. per 1,000 farmers)

5 (3.37)

No. of cases in nonfarmers c (No. per 1,000 non-farmers) 6 (0.55)

Colonrectum f

122 (9.85)

38 (25.66)

84 (7.71)

Skin nonmelanoma

113 (9.12)

39 (26.33)

74 (6.79)

Oesophagu s

5 (0.404)

2 (1.35)

3 (0.275)

CRUDE ANALYSIS Crude OR (95% CI) e P-value d

6.147 (1.482 – 24.208) 0.006 3.389 (2.238 – 5.050) < 0.001 3.954 (2.601 – 5.930) < 0.001 4.910 (----) 0.111

GLMMs OR (95% CI) e P-value d 2.640 (----) 0.131 1.529 (1.011 – 2.314) 0.044 1.844 (1.210 – 2.810) 0.004 Poor or null stratum frequencies g

Pharynx

5 (0.404)

0 (0)

5 (0.460)

Liver + Biliary system f

30 (2.42)

8 (5.40)

22 (2.01)

30

0 (----) n. s. 2.684 (1.031 – 6.274) 0.022

Poor or null stratum frequencies 0.979 (----) 0.9608

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Glands

2 (0.161)

1(0.675)

1(0.092)

Small intestine

1 (0.081)

0 (0)

1 (0.092)

Larynx

7 (0.566)

2 (1.350)

5 (0.459)

Leukaemia

0 (0)

0 (0)

0 (0)

Lymphom as

33 (2.66)

8 (5.41)

25 (2.29)

Breast

98 (7.92)

25 (16.88)

73 (6.70)

Skin melanoma f

27 (2.18)

7 (4.73)

20 (1.84)

Mesotheliu m

4 (0.323)

1 (0.675)

3 (0.275)

Myeloma f

14 (1.131)

4 (2.701)

10 (0.918)

Bones cancer

1 (0.081)

1 (0.675)

0 (0)

7.361 (----) 0.225 0 (----) n.s. 2.945 (----) 0.20 ---2.361 (----) 0.052 2.545 (1.543 – 4.073) < 0.001 2.582 (1.090 – 6.371) 0.035 2.453 (----) 0.399 2.948 (----) 0.076 ---(----) n.s.

Poor or null stratum frequencies Poor or null stratum frequencies Poor or null stratum frequencies Poor or null stratum frequencies 1.323 (----) 0.526 1.720 (1.039 – 2.846) 0.035 1.773 (----) 0.237 Poor or null stratum frequencies 1.461 (----) 0.546 Poor or null stratum frequencies

a

= reference population: 12,378 residents.

b

= number of farmers in the reference population: 1,481 farmers.

c

= number of non-farmers in the reference population: 10,897 non-farmers.

d

= ORs are considered “statistically significant” if P < 0.01, “highly statistically significant” if P

< 0.001 and “suspiciously statistically significant” if

31

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ACCEPTED MANUSCRIPT 0.01 ≤ P < 0.05 (n.s. = not significant, i.e. P ≥ 0.01). Suspiciously statistically significant, statistically significant and highly statistically significant P-values are reported in bold. e

= 95% CI are reported only for ORs with suspiciously statistically significant, statistically

significant and highly statistically significant P-values. f

= GLMMs reduced to a multiple logistic regression model because the variability among the

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Municipalities (random effect) was equal to zero or very small. g

= GLMMs were not converging because of the small number of cancer cases in this body site.

32

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Table 3 (continues) Number of cases by cancer site in the reference population (absolute frequencies and relative frequencies per 1,000 residents), in farmers of the reference population (absolute frequencies and relative frequencies per 1,000 farmers) and in nonfarmers of the reference population (absolute frequencies and relative frequencies per 1,000 non-farmers): results of crude analysis (odds ratios -ORs- with 95% Confidence

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Intervals -CIs- and P-values) and Generalized Linear Mixed Models (GLMMs) analysis (ORs with 95% CIs and P-values). CASES Cancer site

Ovary

f

No. of cases in the reference population1 (No. per 1,000 residents)

No. of cases in farmers2 (No. per 1,000 farmers)

No. of cases in nonfarmers3 (No. per 1,000 non-farmers)

14 (1.131)

5 (3.37)

9 (0.825)

Pancreas f

22 (1.777)

5 (3.376)

17 (1.560)

Penis

1 (0.081)

0 (0)

1 (0.092)

CRUDE ANALYSIS Crude OR (95% CI)5 P-value4

4.097 (1.077 – 13.637) 0.019 2.167 (----) 0.174 0 (----) n.s.

GLMMs OR (95% CI)5 P-value4 2.904 (----) 0.089 1.044 (----) 0.934 Poor or null stratum frequencies g

Peritoneu m

1 (0.081)

0 (0)

1 (0.092)

Lung

84 (6.78)

19 (12.83)

65 (5.96)

Prostate f

71 (5.73)

25 (16.88)

33

46 (4.22)

0 (----) n.s. 2.165 (1.222 – 3.669) 0.006 4.050 (2.376 – 6.752) < 0.001

Poor or null stratum frequencies 0.883 (----) 0.653 1.396 (----) 0.203

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Kidney

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Nervous system f

32 (2.58)

17 (1.37)

11 (7.43)

6 (4.05)

21 (1.92)

11 (1.01)

Stomach

15 (1.21)

5 (3.38)

10 (0.92)

Testicle

3 (0.242)

0 (0)

3 (0.275)

Thymus

2 (0.162)

0 (0)

2 (0.184)

Thyroid f

19 (1.535)

4 (2.701)

16 (1.377)

Uterus f

34 (2.747)

5 (3.376)

29 (2.661)

Bladder

66 (5.33)

10 (6.75)

56 (5.14)

Vulva

6 (0.485)

0 (0)

6 (0.551)

887 (71.66)

241 (162.73)

646 (59.28)

ALL CANCERS

3.874 (1.683 – 8.425) < 0.001 4.025 (1.220 – 11.893) 0.011 3.687 (1.259 – 10.805) 0.026 0 (----) n.s. 0 (----) n.s. 1.964 (----) 0.273 1.269 (----) 0.594 1.316 (----) 0.444 0 (----) n. s. 3.084 (2.618 – 3.624) < 0.001

a

= reference population: 12,378 residents.

b

= number of farmers in the reference population: 1,481 farmers.

c

= number of non-farmers in the reference population: 10,897 non-farmers.

34

1.679 (----) 0.202 2.930 (----) 0.063 1.951 (----) 0.281 Poor or null stratum frequencies Poor or null stratum frequencies 2.105 (----) 0.244 1.095 (----) 0.862 0.519 (----) 0.064 Poor or null stratum frequencies 1.459 (1.229 – 1.731) < 0.001

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ACCEPTED MANUSCRIPT d

= ORs are considered “statistically significant” if P < 0.01, “highly statistically significant” if P

< 0.001 and “suspiciously statistically significant” if 0.01 ≤ P < 0.05 (n.s. = not significant, i.e. P ≥ 0.01). Suspiciously statistically significant, statistically significant and highly statistically significant P-values are reported in bold. e

= 95% CI are reported only for ORs with suspiciously statistically significant, statistically

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significant and highly statistically significant P-values. f

= GLMMs reduced to a multiple logistic regression model because the variability among the

Municipalities (random effect) was equal to zero or very small. g

= GLMMs were not converging because of the small number of cancer cases in this body site.

35

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3 km

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Figure 1 The suburban area of Vercelli: the 18 Municipalities forming the suburban area of Vercelli are included in zones A and B (the zone C represents the Municipality of

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Vercelli).

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Figure 2 Flow diagram of the study

38

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All cancers (P-value < 0.001)

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Breast (P-value = 0.035)

Skin non-melanoma (P-value = 0.004)

Colon-rectum (P-value = 0.044) 0.0

1.5

3.0

4.5

6.0

7.5

9.0

OR

Figure 3 Results of Generalized Linear Mixed Models (GLMMs) analysis: cancer odds ratios (ORs) by cancer site (with 95% Confidence Intervals and P-values) between farmers and non-farmers in the reference population. Only suspiciously statistically significant (0.01 ≤ P < 0.05), statistically significant (P < 0.01) and highly statistically significant (P < 0.001) ORs are reported.

39

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An Italian population-based case-control study on the association between farming and cancer: Are pesticides a plausible risk factor?

This population-based case-control study investigated the association between farming (a proxy for pesticide exposure) and cancer in the Vercelli subu...
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