Mortality from nervous system diseases and cancer in agriculture workers: a case-control study in Italy

Mortality from nervous system diseases and cancer in agriculture workers: a case-control study in Italy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mortality from nervous system diseases and cancer in agriculture workers: a case-control study in Italy Claudio Gariazzo, Alessandro Marinaccio, Dario Consonni, Lucia Miligi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4268499/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Population working or living in agriculture settings may experience important exposure to pesticides and other agents. Some health effects associated with them are well known (e.g. skin cancer due to solar radiation) while for others (e.g., neurological diseases and lymphoid, hematopoietic and related tissue cancers) additional epidemiological evidence is needed. We aim to investigate mortality for neurological diseases and cancer in workers employed in agriculture in Italy. Methods We performed a case-control study based on countrywide Italian mortality data 2005–2018 linked with National Social Insurance data to retrieve information on working histories. Adjusted cancer specific mortality odds ratios (MOR) were calculated. We modelled occupational exposure as “ever/never been employed” in agriculture, using the service sectors as reference. Analysis was stratified for gender and length of employment. Results About 64,000 workers employed in agriculture were analyzed in comparison with a control group of 107,000 workers of the service sector. We found elevated risk in agriculture workers for mortality from spinal muscular atrophy (MOR 1.26, 95% CI: 1.03–1.56; 261 deaths) and Parkinson’s disease (PD) (MOR 1.16, 95% CI:1.00-1.34; 742 deaths). As for cancer mortality, positive associations were found for non-follicular lymphoma (NFL) (MOR 1.59, 95% CI: 1.03–2.46; 82 deaths), multiple myeloma (MM) (MOR 1.42, 95% CI: 1.22–1.65; 546 deaths) and myeloid leukemia (ML) (MOR 1.36, 95% CI:1.16–1.60; 474 deaths), as well as for stomach (MOR 1.30, 95% CI:1.20–1.41; 1,732 deaths), prostate (MOR 2.03, 95% CI:1.85–2.24, 1,582 deaths), and brain and central nervous system cancer (MOR 1.30, 95% CI:1.13–1.50, 601 deaths). PD, NFL and ML, as well as cancers of skin, connective and soft tissue, prostate and brain were found to involve mainly men. Conclusions Long-term employment in agriculture was demonstrated associated with several health risks, some of which could be explained by exposure to pesticides. Although the use of the different agronomic categories of pesticides has been changed over time and some active ingredients were prohibited or limited, their health effects remain of concern for their large use, demanding for further focused investigations and preventive measures. Parkinson’s spinal muscular atrophy leukemia lymphoma brain central nervous system Figures Figure 1 Introduction Work in agriculture includes different job activities such as cultivation, harvesting of crops, rearing animals, and forestry. According to World Bank data, based on International Labour Organization modeled estimates, from year 1991 to 2021 the global workforce employed in agriculture passed from 43 to 26% [ 1 ], mainly driven by the migration of population from countryside to cities, changes in the general economy, cultivation equipment and products market. A similar trend was observed in Italy. According to data provided by the National Statistical Institute (ISTAT), about 3M of workers were employed in agriculture in 1982 with a continuous decreasing trend leading to the about 1M in 2004 [ 2 ]. The number of farms reduced from 3.5M to 1.5M from 1961 to 2010, in agreement with the reduction of the cultivated surface from 26M to 17M hectares [ 3 ]. Among the different occupational or environmental risk factors for those working or living in agriculture settings, pesticides are the most important agents. Additional agents are solar radiation, engine exhausts, solvents, dusts, and zoonotic microbes. The use of pesticides has been ruled by European directives (91/414/ECC and 2009/128/EC) concerning the placing of plant protection products on the market, and a framework for Community action to achieve the sustainable use of pesticides. Pesticides are a broad category, which include insecticides, fungicides, herbicides, plant growth regulators, and other functional categories, with different levels of toxicity determined by the active ingredients and other agents in the composition of commercial products. The use of plant protection products for agricultural use in Italy has decreased from 18,000 tons in 1997 to 5,000 tons in 2018, especially for those with very toxic contents [ 4 ]. However, rural and greenhouse workers, those working in pesticide manufacture, mixing or application, may still have significant pesticides exposure, in addition to that experienced in the past. Also bystanders may be exposed living in proximity of cultivated crop. Moreover, the presence of pesticide residues in water, in foodstuff and in the environment near agriculture farms, involves the general population with a significant background exposure to these compounds. The health effects due to their exposure are well known in literature. The most studied health effect is cancer. The early reviews showed that farmers have a lower risk of most major causes of death than the general population particularly in terms of total mortality, total cancer, health diseases and specific cancers like lung cancer [ 5 – 7 ]. However, according to cohort and case-control studies [ 8 , 9 ], this occupational group has a higher risk for certain types of cancers, particularly Soft Tissue Sarcomas (STSs), non-Hodgkin lymphomas (NHLs) [ 10 – 13 ], Hodgkin’s disease (HD), leukemia, multiple myeloma (MM) [ 14 ], prostate cancer [ 15 – 18 ] and cancer of the skin and lip [ 5 , 6 ]. In Italy several multicenter case-control studies have investigated the association between agricultural work, pesticides exposure and risk of cancer [19–23; 10, 11, 14]. Others examined the association between cancer and the exposure to pesticides of licensed users [ 24 – 26 ]. The potential of pesticides as human carcinogens has been highlighted by international agencies [ 27 , 28 ]. However, not all studies agreed on the association between agricultural work and cancer-specific outcomes, depending on the type of study, study area, workers involved, job-related activities, gender- or age-based differences, available exposure data and working histories. Some studies have suggested that pesticides may cause other types of cancer, such as brain cancer [ 29 – 31 ], but others not [ 32 – 34 ]. In the recent and past literature, exposure to agriculture agents like pesticides has also been associated with neurodegenerative diseases [ 31 , 35 – 41 ]. Parkinson’s disease, amyotrophic lateral sclerosis (ALS) and Alzheimer’s disease are the most common neurodegenerative disorders, that has been related with exposure to pesticide with an increase of risk by at least 50% [ 42 – 44 ]. More recent studies involved other health effects such as epilepsy [ 38 ], tremor [ 39 ] and neurobehavioral, neuromotor, and neurocognitive effects [ 40 ]. Not all studies agreed on the association between agricultural work and increased risk of brain disease [ 35 , 45 , 46 ]. Misclassification and inadequate response rates are considered as possible explanations for the non-association observed in some studies [ 47 , 48 ]. Nevertheless, the scientific evidence suggests an association between neoplastic and neurological effects in farmers exposed to pesticides and other agents used in agriculture, the question related to the exposure assessment became increasingly complex and need further studies to better understand some aspects of the topic and the association of past exposure with health outcomes. There is a lack of a comprehensive assessment of occupational risks in agriculture, as most studies focus on specific regions of a country with a limited population enrolled, primarily in cohort studies, as well as on neurodevelopmental disorders and diseases of the nervous system other than Parkinson’s. This study aims to fulfill this lack of information by using a case-control study design, based on national registered mortality data in the period 2005–2018 integrated with working histories derived from the National Social Insurance archive. Methods Study design We used a case-control study based on countrywide mortality data covering the period 2005–2018 [ 49 ]. We selected as cases different causes of death likely associated to main occupational risk factors in agriculture including neuro-degenerative and malignant neoplasm. For each cause of death, we selected as controls the causes of death out of the one under study. We defined deceased workers who had worked in the agricultural sector as exposed, while those who had worked in the service sector were considered unexposed, since assumed to be representative of the general unexposed population. Mortality data Mortality data from year 2005 to 2018, coded according to the tenth International Classification of Diseases (ICD-10) were retrieved from ISTAT. We selected the following groups of causes of death of interest: mental, behavioral and neurodevelopmental disorders (F01-F99); diseases of the nervous system (G00-G99); and malignant neoplasms(C00-C96). Among these groups, we selected a few specific diseases for their particular interest in this field of study. A detailed list of diseases is shown in Tables 3 , 4 and 5 . Mortality data included information on gender, age of death, which was classified in 15 classes (< 19, 20–84 in 5 years and 85+), educational level (2 classes: middle and primary school), and date and location of death. Occupational data We used the information on working history retrieved from the National Social Insurance Agency (INPS). The data consists of about 55% of the Italian workforce and do not include data on public employment, self-employment, artisans, domestic workers, para-subordinate workers, and occasional workers. Data consist of employment periods from 1974 onwards spent by the worker in each private sector in his/her working-life [ 49 ]. Information about the specific occupations was not available except for a blue-collar indication. Economic sectors in INPS files are classified according the ATECO 81. We classified economic activities according to the Statistical Classification of Economic Activities in the European Community, NACE Rev. 2, grouped into broader categories data [ 50 ] for a total of 48 sectors of employment. Among them, we selected those deaths related to persons who worked in the agricultural and in the service sectors. The latter is used as reference population and includes wholesale and retail trade, accommodation and food service activities, financial and insurance activities, administrative and support service activities. The occupational data were linked at individual level with the mortality ones using the tax identification code by authorized personnel. The linked dataset was then anonymized, stored, and processed under strict controls to protect personal data. To maximize the etiological link between causes of death and potential occupational exposures, we carried out a number of data selection procedures. First, for both cases and controls we selected blue-collar workers with low educational level (primary or middle school), because they are more likely to have been engaged in jobs and tasks entailing exposure to various agents in agriculture. Second, we calculated the median value of the years of employment for each economical sector and only those cases with a duration of employment higher than that value (permanence criteria) were selected. Third, in order to avoid that multiple sectors of employment could confound the relationship between the cause of death and the specific sector, we selected cases in which the length of employment in a specific sector was prevalent (defined as the sector with at least 75% of the total years of registered working activities). In summary, only cases and controls with a blue-collar definition, low educated and with a sufficient (permanence criteria) and prevalent length of employment in a specific sector were included in the analysis. Figure 1 shows the flow chart of process of selecting the study population. Statistical analyses We fitted logistic regression models to calculate mortality odds ratios (MORs) and 95% confidence intervals (CI) for various causes of death. Occupational exposure was modelled as “ever/never been employed” in which workers employed in the agriculture were considered as exposed, and those ones in the services as unexposed (reference). The models were adjusted for gender, age class, educational level, year of death and region of residence, the latter included to consider the heterogeneities of cultivations in the national territory. We also performed analyses stratified by gender and length of employment 0–30; 30 + years. For simplicity we did not perform lagged analyses because we examined a large spectrum of outcomes, each requiring a different lag. Finally, a sensitivity analysis was carried out to investigate about the impact of selecting the service sector as reference. The main analysis was repeated using all other sectors (excluding the agriculture one) as a reference. Results Among the 1.6M of blue-collar workers deaths during the years 2005–2018, about 64K and 107K worked in the agriculture and service sectors with a sufficient (permanence criteria) and prevalent length of employment, respectively (Table 1 ). Most of those who worked in agriculture were men (75%) with a primary school diploma (85%) and started working at a median age of 39 and continued to work for a median of 31 years (Table 2 ). This result is consistent with the start date of working history (1974) and with the tendency of farmers in beginning work at early age and ending it very late. The control group (workers in the service sector) were balanced in terms of gender (52% vs 48%), but with higher age at first employment (46 as median value) and with shorter length of employment (6 years as median value) than those who worked in the agriculture sector (Table 2 ). Most of the workers were employed in agriculture from 1974 to 2004 (table S1 of supplementary materials (SM)). The number of agricultural workers by period of working history and disease of death is shown in table S3 of SM. Table 3 shows MORs results for mental, behavioural and neurodevelopmental disorders and nervous system diseases in workers employed in the agricultural sector compared to those in services. We found positive associations between employment in agriculture and spinal muscular atrophy (SMA) and Parkinson's diseases (in men only). Most malignant neoplasms of lymphoid, hematopoietic and related tissue were in excess in those ever employed in the agricultural sector, particularly in men (Table 4 ). Malignant immunoproliferative diseases emerged in women only but based on a few cases. Multiple myeloma and lymphoid or unspecified leukemias were in excess in both genders. We found positive associations between employment in agriculture and mortality from several other cancers, including cancer of lip, stomach, skin melanoma, colon-rectum, connective and soft tissue, prostate, kidney, brain and central nervous system (Table 5 ). Reduced risks were found for many causes of death, including multiple sclerosis, malignant neoplasms of liver, larynx, lung, ovary and bladder (Tables 3 and 5 ). Some elevated risks emerged only in men (lip cancer, melanoma of skin, malignant neoplasm of connective and soft tissue and of brain and central nervous system) Cancers of stomach, colon-rectum, and kidney were positively associated with work in agriculture in both genders, although with MORs of different magnitude. For malignant neoplasms of brain, skin, stomach, colon-rectum, kidney, and prostate we observed positive trends of risk with the length of employment (table S3 of SM). Conversely, risks of lip cancer, multiple myeloma, myeloid or unspecified leukemia, cancer of ovary, Parkinson's disease, and spinal muscular atrophy showed lower risks for those who worked < 30 years in agriculture. The sensitivity analyses about the relative importance of the reference sector shows that the pattern of identified risks did not in general change when all other occupational sectors are used as reference (table S4). Discussion In this nationwide study, we analyzed the causes of death of more than 64k workers employed in the agriculture in comparison with a control group of 107K workers of the service sector. We found agricultural workers to be associated with mortality for neurological and malignant cancer-specific diseases. In particular, we identified in the group of neurological diseases, associations with mortality for spinal muscular atrophy and Parkinson’s disease, while in the lymphoid, hematopoietic and related tissue cancer group, we found associations with lymphoma, myeloma and leukemia diseases. In the malignant solid neoplasm group, we also identified positive associations in agricultural workers with mortality for cancer in stomach, colon-rectum, skin, connective and soft tissue, prostate, kidney and brain and central nervous system. An evident tendency towards an increased risk for cancer of lip was found for men only, likely caused by exposure to solar radiation. Pesticides are considered the main agent of exposure for agricultural workers. They are specifically manufactured and used to eradicate or control undesired organisms. Insecticides, herbicides, fungicides, nematocides, rodenticides and other preparations are the pesticides for which there is a higher concern for occupational health [ 28 ]. Best-known toxic mechanisms of pesticides are: interference with axonal nerve conduction; synaptic transmission; mitochondrial respiration; steroid biosynthesis; blood coagulation [ 28 ]. The main target organs are the skin, the eyes, the mucosae of the respiratory tract, the digestive apparatus, and the nervous system. Other chronic effects involve liver and kidney toxicity, as well as chronic effects on the skin, on immune, respiratory, and endocrine systems, on blood, and on peripheral and central nervous system [ 28 ]. The results of this study are mostly in line with the ILO’s list of occupational diseases caused by pesticides exposure, and with the past and recent literature in this field [ 5 – 7 , 15 , 17 , 29 , 31 , 35 – 41 , 51 , 52 ]. As for the diseases of the nervous system, we found farmers, and male in particular, to be at risk for Parkinson’s diseases. The evidence of the association between working in agriculture and Parkinson’s diseases is large but also controversial. In a review by Nandipati and Litvan [ 37 ], the authors addressed that among the different pesticides, rotenone, paraquat, and organochlorines have been well-documented in human epidemiological studies to be associated with Parkinson’s diseases, while organophosphates, pyrethroids, and polychlorinated biphenyls require further study. In a more recent systematic review, Sturm et al. [ 35 ] examined 22 studies about the relationship between agricultural work and Parkinson’s disease or Parkinsonism but they did not all agree on the presence of an association. Positive associations were found in studies carried out in France [ 41 ], Norway [ 53 ], US [ 54 ] and Spain [ 36 ]. In Italy one study concerning farming and Parkinson’s disease found no association [ 47 ]. Positive association was instead found in a case-control study in north-east of Italy [ 55 ]. An important genetic influence seems to be a factor [ 56 – 58 ]. Our study found an association between working in agriculture and mortality for the group of spinal muscular atrophy diseases (SMA) without gender difference. A few studies found an association between pesticide exposure and the development of ALS, a disease part of SMA group [ 59 – 61 ]. In Italy there is poor information about this disease in connection with working in agriculture. Filippini et al. [ 62 ] in a population-based case-control study in four Italian provinces, found no statistically significant positive association with ALS risk in agricultural workers. The results of our study, are in line with the scientific literature and add further evidence to this field of study. However, when stratified by sex this association loss of statistical significance. We did not find associations with mortality for Alzheimer’s disease, multiple sclerosis, and epilepsy, although these health effects were positively associated with exposure to pesticides in Spain [ 36 , 38 ] and in a review by Gangemi et al. [ 59 ]. At the same time, we did not find any association with mental, behavioral and neurodevelopmental disorders as a whole, and in vascular and unspecified dementia. One possible reason was the use of mortality data instead of hospitalization data of these diseases, which could have masked the associations. As for cancer effects caused by long-term exposure to agents during working in agriculture, some studies have suggested an association between pesticide exposure and Hodgkin's disease, leukaemia, non-Hodgkin lymphoma, multiple myeloma (5,6, 10–14) as well as tumors of the lip, stomach, prostate, skin, brain, and connective tissues [ 5 , 6 , 31 , 35 ]. The International Agency for Research on Cancer (IARC), evaluating the cancer risk associated with non-arsenical insecticide spraying, has concluded that this activity is probably carcinogenic to humans (Group 2A) and recently some active ingredients have been evaluated as certain or probable carcinogens for humans [ 27 , 63 ], likely causing an increased risk for skin, lymphatic, and lung cancer [ 27 , 28 , 63 ]. However, no pesticide has sufficient evidence in humans to be classified as a brain or central nervous system carcinogen by IARC [ 27 , 64 ]. The results of our study confirm the above evidence of cancer effects related to agricultural workers, providing evidence of risk for specific cancer types. As for brain cancer, our results are in line with a study about mortality in male farmers licensed to use pesticides [ 24 ], with an Italian case-control study on farmer work and the cancer morbidity [ 65 ], and with some recent reviews [ 31 , 35 ]. Our study provides additional information about its prevalence in men workers, and in farmers who worked for more than 30 years. In Italy both cohort and case-control studies were conducted to investigate cancer effects among agricultural workers. Faustini et al. [ 66 ] carried out a cohort-based mortality study of farm workers, in which no statistically significant excesses were observed in cancer mortality. A cohort of licensed pesticide users was investigated by Figà-Talamanca et al. [ 25 ] resulting in a standardized mortality ratio (SMR) of 72 for all cancers. An increase of risk for melanomas and eye tumors were observed in a cohort of farmers and for lymphoma and tumors of the connective tissue in the sub cohort of subjects living in villages with mainly arable land [ 67 ]. By comparing cancer risk mortality among two cohorts of Danish and Italian farmers, Ronco et al. [ 68 ] found a consistent risk reduction for cancer of the lung, bladder, small intestine, colon, rectum, and prostate, but an excess risk for leukemia in female only, and a slight excess risk for non-Hodgkin's lymphoma. Among the case-control studies, an early mortality study about farmers in central Italy found no association with NHL, Hodgkin's lymphoma, multiple myeloma, and leukemia but when the different crops are considered some increasing risks are observed [ 69 ]. The findings of other Italian studies were more in agreement with our results about lymphoid, hematopoietic and related tissue cancer group. Among them a study carried out in a north area of Italy found increasing risk for NHL and chronic lymphocytic leukemia [ 19 ]. A multicenter case-control study suggested an increased risk for NHL and leukemia, and some chemical classes of pesticides, although few are statistically significant and some are based on few exposed cases [ 21 ]. As in our study, no statistically significant increased risk of NHL was found in a population-based case-control study carried out in Italy and involving many agricultural and mixed areas, in which exposure in agriculture was assessed by the use of a specific questionnaire given to subjects who were exposed to phenoxy herbicides not using protective equipment [ 23 ]. A hospital-based case-control study was conducted in five Italian rural areas by Settimi et al. [ 20 ], which investigated the association between cancer and farming among male agricultural workers. They found increased risks of cancer associated with agricultural workers for stomach, rectum, larynx and prostate, in agreement with the findings of this study out of the larynx one. The above Italian studies show a large spectrum of specific positive associations results, often with inconsistencies and disagreement likely due to specific peculiarities of each study. Among them we could mention: type of study (eg. cohort and case-control), peculiarities in the sample of farmers (eg. population, pesticide licensed users), studied area (cohort limited to specific Provinces, multicenter) and their representativeness at national level, different job activities (eg. cultivation, harvesting of crops), methods of farmer identification (questionnaire, registries, census), exposure assessment and heterogeneities of cultivations at regional level. It's worth noting that the healthy worker effect or, the healthier lifestyle of farm families, can be easily observed for agricultural workers and can lead to an attenuation of the risk of death. Although this study provides new and additional associations between health effects and agricultural workers in Italy, it contains few limitations that must be considered. The first is that occupational administrative data cannot adequately characterize occupational exposure profiles. INPS data report the industrial sector but do not contain information on workers’ roles in a specific sector or exposure variables such as exposure intensity. In addition, the complexity of exposure pattern, the difficulty in reconstructing past exposure, considering the change over time in specific chemical use, and the variation in work practices, should be taken into account. Consequently, occupation data of the present study only concern about the working-time spent in specific sectors, which might be involved with exposure to agents possibly causing diseases. Therefore, we used a rather broad classification of agricultural workers, not considering important factors for the exposure assessment like: specific job-related activities; heterogeneity of agricultural work itself (eg. crop type, animal type); type of pesticide applied; complex synergies among risks; and actual dose of exposure. We also not considered individual factors like genetic, lifestyles, socioeconomic status and co-morbidities, as well as external factors like environmental differences. We could not account for latency of the specific outcomes related to the exposure agents due to lack of information. Nevertheless, this study provides a national picture of neurological and cancer risks that could be experienced by workers in agriculture in Italy, overcoming the limited information and representativeness provided by former case-control and cohort studies. Performing a record linkage of administrative data, and in particular between INPS files and mortality data, allowed us to fill the gap of the misclassification of the occupational information derived by questionnaire or census. To enhance the etiological relationship between the sector of employment and health outcome, we adopt a selection criterion based on a prevalent sector of employment, and a sector specific duration of it. Moreover, it was possible to establish an epidemiological study with high statistical power and to study the excess mortality risks among men and women separately. Conclusions Working in agriculture for long term was demonstrated to produce health risks for workers likely due to occupational and environmental exposure to pesticides and other agents here located. Although their use has been quantitative and qualitative limited, their health effects remain of concern for their large use in the past. Despite the limits of this study, it found similar increase of risks for specific diseases, part of them already identified by several studies. The findings of this study demand for further focused limitation measures, especially for those belonging to classes of compounds for which there are some specific risks for human health. Possible actions should be oriented to the substitution of toxic and persistent active substances with less active, more selective and less persistent ones. Declarations Ethics approval and consent to participate: Not applicable due to the use of administrative data. Consent for publication: Data access was granted by the National Statistics Program (PSN 2020-2022), which provides a prior authorization with the force of law of the Italian Data Protection Authority for statistical works of public interest. Availability of data and materials: The authors do not have permission to share data Competing interests: None Declared. Funding: This work was supported by the National Institute for the Insurance of Occupational Injuries (INAIL) in the framework of the 2022–2024 INAIL Scientific Research Plan. Authors' contributions: Claudio Gariazzo: Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Writing - Review & Editing; Stefania Massari: Conceptualization, Data Curation; Methodology, Formal analysis, Writing - Review & Editing; Dario Consonni: Methodology, Writing - Review & Editing; Lucia Miligi: Writing - Review & Editing; Alessandro Marinaccio: Conceptualization, Methodology, Writing - Review & Editing. Acknowledgement : Access to data was granted by the fact that this activity was included in the National Statistics Program (PSN 2017–2019) which provides a prior authorization with the force of law of the Italian Data Protection Authority for statistical surveys of public interest. All epidemiological analyses were performed using anonymous data. References Word Bank 2021. Employment in Agriculture (% of Total Employment) (Modeled ILO Estimate)|Data. Available online: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS (accessed on 14 December 2023). Istat, 2023a, https://www.istat.it/it/censimenti/censimenti-precedenti/agricoltura. Istat, 2023b, Censimenti generali dell'agricoltura. https://seriestoriche.istat.it/index.php?id=1&no_cache=1&tx_usercento_centofe%5Bcategoria%5D=13&tx_usercento_centofe%5Baction%5D=show&tx_usercento_ centofe%5Bcontroller%5D=Categoria&cHash=e3503d8195dd4231ff53ba078ad5c124 ISTAT, 2023c. http://dati.istat.it/Index.aspx?DataSetCode=DCSP_FITOSANITARI# Blair A, Malker H, Cantor KP, Burmeister L, Wiklund K. Cancer among farmers. A review. Scand J Work Environ Health. 1985 Dec; 11(6):397-407. doi: 10.5271/sjweh.2208. Blair A and Zahm S H. Agricultural Exposures and Cancer. Environ Health Perspect 103(Suppl 8):205-208 (1995) Acquavella J., Olsen G., Cole P., Ireland B., Kaneene J., Schuman S., Holden L. Cancer among farmers: A meta-analysis. Annals of Epidemiology, 1998,8 (1), 64 – 74. DOI: 10.1016/S1047-2797(97)00120-8. Lerro, C.C., Koutros, S., Andreotti, G. et al. Cancer incidence in the Agricultural Health Study after 20 years of follow-up. Cancer Causes Control 30, 311–322 (2019). https://doi.org/10.1007/s10552-019-01140-y. Togawa K, Leon ME, Lebailly P, Beane Freeman LE, Nordby KC, Baldi I, MacFarlane E, Shin A, Park S, Greenlee RT, Sigsgaard T, Basinas I, Hofmann JN, Kjaerheim K, Douwes J, Denholm R, Ferro G, Sim MR, Kromhout H, Schüz J. Cancer incidence in agricultural workers: Findings from an international consortium of agricultural cohort studies (AGRICOH). Environ Int. 2021 Dec;157:106825. doi: 10.1016/j.envint.2021.106825. De Roos AJ, Schinasi LH, Miligi L, et al. Occupational insecticide exposure and risk of non-Hodgkin lymphoma: A pooled case-control study from the InterLymph Consortium. Int. J. Cancer. 2021; 149(10): 1768-1786. https://doi.org/10.1002/ijc.33740. De Roos AJ, Fritschi L, Ward MH, et al. Herbicide use in farming and other jobs in relation to non-Hodgkin’s lymphoma (NHL) risk. Occupational and Environmental Medicine 2022;79:795-806. Leon, M.E., Schinasi L. H., Lebailly P., et al. Pesticide use and risk of non-Hodgkin lymphoid malignancies in agricultural cohorts from France, Norway and the USA: a pooled analysis from the AGRICOH consortium, International Journal of Epidemiology, Volume 48, Issue 5, October 2019, Pages 1519–1535, https://doi.org/10.1093/ije/dyz017 Schinasi, L.; Leon, M.E. Non-Hodgkin Lymphoma and Occupational Exposure to Agricultural Pesticide Chemical Groups and Active Ingredients: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2014, 11, 4449-4527. https://doi.org/10.3390/ijerph110404449. Perrotta C, Kleefeld S, Staines A, Tewari P, De Roos AJ, Baris D, Birmann B, Chiu B, Cozen W, Becker N, Foretova L, Maynadié M, Nieters A, de Sanjosé S, Miligi L, Seniori Costantini A, Purdue M, Spinelli J, Cocco P. Multiple myeloma and occupation: a pooled analysis by the International Multiple Myeloma Consortium. Cancer Epidemiol. 2013 Jun;37(3):300-5. doi: 10.1016/j.canep.2013.01.008. Pardo LA, Beane Freeman LE, Lerro CC, Andreotti G, Hofmann JN, Parks CG, Sandler DP, Lubin JH, Blair A, Koutros S. Pesticide exposure and risk of aggressive prostate cancer among private pesticide applicators. Environ Health. 2020 Mar 5;19(1):30. doi: 10.1186/s12940-020-00583-0. Koutros, S., Beane Freeman, L.E., Lubin, J.H. et al. Risk of total and aggressive prostate cancer and pesticide use in the Agricultural Health Study. American Journal of Epidemiology, 2013, 177(1), pp. 59–74. Krestv S, Knutsson A. Occupational Risk Factors for Prostate Cancer: A Meta-analysis. J Cancer Prev. 2019 Jun;24(2):91-111. doi: 10.15430/JCP.2019.24.2.91. Lewis-Mikhael AM, Bueno-Cavanillas A, Ofir Giron T, Olmedo-Requena R, Delgado-Rodríguez M, Jiménez-Moleón JJ. Occupational exposure to pesticides and prostate cancer: a systematic review and meta-analysis. Occup Environ Med. 2016 Feb;73(2):134-44. doi: 10.1136/oemed-2014-102692. Nanni O, Amadori D, Lugaresi C, Falcini F, Scarpi E, Saragoni A, Buiatti E. Chronic lymphocytic leukaemias and non-Hodgkin's lymphomas by histological type in farming-animal breeding workers: a population case-control study based on a priori exposure matrices. Occup Environ Med. 1996 Oct;53(10):652-7. doi: 10.1136/oem.53.10.652. 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 Health. 2001;14(4):339-47. Miligi, L., Costantini, A.S., Bolejack, V., Veraldi, A., Benvenuti, A., Nanni, O., Ramazzotti, V., Tumino, R., Stagnaro, E., Rodella, S., Fontana, A., Vindigni, C. and Vineis, P. (2003), Non-Hodgkin's lymphoma, leukemia, and exposures in agriculture: Results from the Italian multicenter case-control study. Am. J. Ind. Med., 44: 627-636. https://doi.org/10.1002/ajim.10289 Cocco P, Satta G, D'Andrea I, Nonne T, Udas G, Zucca M, Mannetje A', Becker N, Sanjosé Sd, Foretova L, Staines A, Maynadié M, Nieters A, Brennan P, Ennas MG, Boffetta P. Lymphoma risk in livestock farmers: results of the Epilymph study. Int J Cancer. 2013 Jun 1;132(11):2613-8. doi: 10.1002/ijc.27908. Miligi L, Costantini AS, Veraldi A, Benvenuti A; WILL; Vineis P. Cancer and pesticides: an overview and some results of the Italian multicenter case-control study on hematolymphopoietic malignancies. Ann N Y Acad Sci. 2006 Sep;1076:366-77. doi: 10.1196/annals.1371.036. Alberghini V, Luberto F, Gobba F, Morelli C, Gori E, Tomesani N. Mortality among male farmers licensed to use pesticides. Med Lav. 1991 Jan-Feb;82(1):18-24. PMID: 1865842. Figa-Talamanca, Mearelli I., Valente P., Bascherini S., Cancer Mortality in a Cohort of Rural Licensed Pesticide Users in the Province of Rome, International Journal of Epidemiology, Volume 22, Issue 4, August 1993, Pages 579–583, https://doi.org/10.1093/ije/22.4.579 Sperati A, Rapiti E, Settimi L, Quercia A, Terenzoni B, Forastiere F. Mortality among male licensed pesticide users and their wives. Am J Ind Med. 1999 Jul;36(1):142-6. doi: 10.1002/(sici)1097-0274(199907)36:13.0.co;2-e. PMID: 10361599. International Agency for Research on Cancer. Some Organophosphate Insecticides and Herbicides; IARC Monographs on the Evaluation of Carcinogenic Risks to Humans Vol. 112; IARC, World Health Organization: Lyon, France, 2017, ISBN 978-92-832-0178-6. International Labour Organization (ILO). Diagnostic and exposure criteria for occupational diseases – Guidance notes for diagnosis and prevention of the diseases in the ILO List of Occupational Diseases (revised 2010). ISBN 978-92-2-035683-8, 2022. Available at: https://www.ilo.org/global/topics/geip/WCMS_836362/lang--en/index.htm Khuder, S.A., Mutgi, A.B. and Schaub, E.A. (1998), Meta-analyses of brain cancer and farming. Am. J. Ind. Med., 34: 252-260. https://doi.org/10.1002/(SICI)1097-0274(199809)34:33.0.CO;2-X Piel C, Pouchieu C, Tual S, Migault L, Lemarchand C, Carles C, Boulanger M, Gruber A, Rondeau V, Marcotullio E, Lebailly P, Baldi I; AGRICAN group. Central nervous system tumors and agricultural exposures in the prospective cohort AGRICAN. Int J Cancer. 2017 Nov 1;141(9):1771-1782. doi: 10.1002/ijc.30879. Gatto NM, Ogata P, Lytle B. Farming, Pesticides, and Brain Cancer: A 20-Year Updated Systematic Literature Review and Meta-Analysis. Cancers (Basel). 2021 Sep 5;13(17):4477. doi: 10.3390/cancers13174477. Ruder AM, Waters MA, Butler MA, Carreón T, Calvert GM, Davis-King KE, Schulte PA, Sanderson WT, Ward EM, Connally LB, Heineman EF, Mandel JS, Morton RF, Reding DJ, Rosenman KD, Talaska G; Brain Cancer Collaborative Study Group. Gliomas and farm pesticide exposure in men: the upper midwest health study. Arch Environ Health. 2004 Dec;59(12):650-7. doi: 10.1080/00039890409602949. Pouchieu C., I. Baldi, A. Gruber, E. Berteaud, C. Carles, H. Loiseau. Descriptive epidemiology and risk factors of primary central nervous system tumors: Current knowledge. Revue Neurologique, Volume 172, Issue 1, 2016, Pages 46-55, https://doi.org/10.1016/j.neurol.2015.10.007. Preston-Martin, S.; Lewis, S.; Winkelmann, R.; Borman, B.; Auld, J.; Pearce, N. Descriptive epidemiology of primary cancer of the brain, cranial nerves, and cranial meninges in New Zealand, 1948–88. Cancer Causes Control 1993, 4, 529–538. Sturm, E.T., Castro, C., Mendez-Colmenares, A. Duffy, J. , Burzynska, A.Z.; Stallones, L., Thomas, M.L. Risk Factors for Brain Health in Agricultural Work: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 3373. https://doi.org/10.3390/ijerph19063373 Parrón T., Mar Requena, Antonio F. Hernández, Raquel Alarcón. Association between environmental exposure to pesticides and neurodegenerative diseases, Toxicology and Applied Pharmacology, Volume 256, Issue 3, 2011, 379-385, https://doi.org/10.1016/j.taap.2011.05.006. Nandipati S, Litvan I. Environmental Exposures and Parkinson's Disease. Int J Environ Res Public Health. 2016 Sep 3;13(9):881. doi: 10.3390/ijerph13090881. Alarcón R, Giménez B, Hernández AF, López-Villén A, Parrón T, García-González J, Requena M. Occupational exposure to pesticides as a potential risk factor for epilepsy. Neurotoxicology. 2023 May;96:166-173. doi: 10.1016/j.neuro.2023.04.012. Dardiotis E, Skouras P, Varvarelis OP, Aloizou AM, Hernández AF, Liampas I, Rikos D, Dastamani M, Golokhvast KS, Bogdanos DP, Tsatsakis A, Siokas V, Mitsias PD, Hadjigeorgiou GM. Pesticides and tremor: An overview of association, mechanisms and confounders. Environ Res. 2023 Jul 15;229:115442. doi: 10.1016/j.envres.2023.115442. Lucero B and Muñoz-Quezada MT. Neurobehavioral, Neuromotor, and Neurocognitive Effects in Agricultural Workers and Their Children Exposed to Pyrethroid Pesticides: A Review. Front. Hum. Neurosci. 15:648171. doi: 10.3389/fnhum.2021.648171 Perrin L, Spinosi J, Chaperon L, Kab S, Moisan F, Ebaz A. Pesticides expenditures by farming type and incidence of Parkinson disease in farmers: A French nationwide study. Environ Res. 2021 Jun;197:111161. doi: 10.1016/j.envres.2021.111161. Gunnarsson LG, Bodin L. Parkinson's disease and occupational exposures: a systematic literature review and meta-analyses. Scand J Work Environ Health. 2017 May 1;43(3):197-209. doi: 10.5271/sjweh.3641. Gunnarsson LG, Bodin L. Amyotrophic Lateral Sclerosis and Occupational Exposures: A Systematic Literature Review and Meta-Analyses. Int J Environ Res Public Health. 2018 Oct 26;15(11):2371. doi: 10.3390/ijerph15112371. Gunnarsson LG, Bodin L. Occupational Exposures and Neurodegenerative Diseases-A Systematic Literature Review and Meta-Analyses. Int J Environ Res Public Health. 2019 Jan 26;16(3):337. doi: 10.3390/ijerph16030337. Kyrozis, A.; Ghika, A.; Stathopoulos, P.; Vassilopoulos, D.; Trichopoulos, D.; Trichopoulou, A. Dietary and lifestyle variables in relation to incidence of Parkinson’s disease in Greece. Eur. J. Epidemiol. 2013, 28, 67–77. Firestone, J.A.; Lundin, J.I.; Powers, K.M.; Smith-Weller, T.; Franklin, G.M.; Swanson, P.D.; Longstreth, W.; Checkoway, H. Occupational factors and risk of Parkinson’s disease: A population-based case–control study. Am. J. Ind. Med. 2009, 53, 217–223. Rocca, W.A.; Anderson, D.W.; Meneghini, F.; Grigoletto, F.; Morgante, L.; Reggio, A.; Savettieri, G.; Di Perri, R. Occupation, education, and Parkinson’s disease: A case-control study in an Italian population. Mov. Disord. 1996, 11, 201–206. Kab, S.; Moisan, F.; Elbaz, A. Farming and incidence of motor neuron disease: French nationwide study. Eur. J. Neurol. 2017, 24, 1191–1195. Massari S., Malpassuti V.C., Binazzi A., Paris L., Gariazzo C., Marinaccio A. Occupational Mortality Matrix: A Tool for Epidemiological Assessment of Work-Related Risk Based on Current Data Sources. Int. J. Environ. Res. Public Health 2022, 19, 5652. Eurostat. NACE Rev. 2—Statistical Classification of Economic Activities in the European Community; Eurostat: Luxembourg, 2008; ISBN 978-92-79-04741-1. Alavanja MC, Bonner MR. Occupational pesticide exposures and cancer risk: a review. J Toxicol Environ Health B Crit Rev. 2012;15(4):238-63. doi: 10.1080/10937404.2012.632358. PMID: 22571220; PMCID: PMC6276799. Alavanja, M.C.R., Ross, M.K. and Bonner, M.R. (2013), Increased cancer burden among pesticide applicators and others due to pesticide exposure. CA: A Cancer Journal for Clinicians, 63: 120-142. https://doi.org/10.3322/caac.21170 Skeie, G.; Muller, B.; Haugarvoll, K.; Larsen, J.; Tysnes, O. Differential effect of environmental risk factors on postural instability gait difficulties and tremor dominant Parkinson’s disease. Mov. Disord. 2010, 25, 1847–1852. Kirkey, K.L.; Johnson, C.C.; Rybicki, B.A.; Peterson, E.L.; Kortsha, G.X.; Gorell, J.M. Occupational categories at risk for Parkinson’s disease. Am. J. Ind. Med. 2001, 39, 564–571. Zorzon, M.; Capus, L.; Pellegrino, A.; Cazzato, G.; Zivadinov, R. Familial and environmental risk factors in Parkinson’s disease: A case-control study in north-east Italy. Acta Neurol. Scand. 2002, 105, 77–82. Glass, T.; Dalvie, M.A.; Holtman, Z.; Vorster, A.A.; Ramesar, R.S.; London, L. DNA variants and organophosphate neurotoxicity among emerging farmers in the Western Cape of South Africa. Am. J. Ind. Med. 2017, 61, 11–20. Dutheil, F.; Beaune, P.; Tzourio, C.; Loriot, M.-A.; Elbaz, A. Interaction between ABCB1 and Professional Exposure to Organochlorine Insecticides in Parkinson Disease. Arch. Neurol. 2010, 67, 739–745. Ritz, B.R.; Manthripragada, A.D.; Costello, S.; Lincoln, S.J.; Farrer, M.J.; Cockburn, M.; Bronstein, J. Dopamine Transporter Genetic Variants and Pesticides in Parkinson’s Disease. Environ. Health Perspect. 2009, 117, 964–969. Gangemi S, Miozzi E, Teodoro M, Briguglio G, De Luca A, Alibrando C, Polito I, Libra M. Occupational exposure to pesticides as a possible risk factor for the development of chronic diseases in humans (Review). Mol Med Rep. 2016 Nov;14(5):4475-4488. doi: 10.3892/mmr.2016.5817. Kamel F, Umbach DM, Bedlack RS, Richards M, Watson M, Alavanja MC, Blair A, Hoppin JA, Schmidt S and Sandler DP: Pesticide exposure and amyotrophic lateral sclerosis. Neurotoxicology 33: 457 462, 2012. Kang H, Cha ES, Choi GJ and Lee WJ: Amyotrophic lateral sclerosis and agricultural environments: A systematic review. J Korean Med Sci 29: 1610 1617, 2014. Filippini T, Tesauro M, Fiore M, et al. Environmental and Occupational Risk Factors of Amyotrophic Lateral Sclerosis: A Population-Based Case-Control Study. Int J Environ Res Public Health. 2020 Apr 22;17(8):2882. doi: 10.3390/ijerph17082882. International Agency for Research on Cancer. DDT, Lindane, and 2,4-D; IARC Monographs on the Evaluation of Carcinogenic Risks to Humans Volume 113; IARC, World Health Organization: Lyon, France, 2016, ISBN 978-92-832-0179-3. Cogliano, V.J.; Baan, R.; Straif, K.; Grosse, Y.; Lauby-Secretan, B.; El Ghissassi, F.; Bouvard, V.; Benbrahim-Tallaa, L.; Guha, N.; Freeman, C.; et al. Preventable exposures associated with human cancers. J. Natl. Cancer Inst. 2011, 103, 1827–1839. Fallahi P, Foddis R, Cristaudo A, Antonelli A. High risk of brain tumors in farmers: a mini-review of the literature, and report of the results of a case control study. Clin Ter. 2017 Sep-Oct;168(5):e290-e292. doi: 10.7417/T.2017.2022. Faustini A, F Forastiere, L Di Betta, E M Magliola, C A Perucci. Cohort study of mortality among farmers and agricultural workers. Med Lav. 1993 Jan-Feb;84(1):31-41. Torchio P, Lepore AR, Corrao G, Comba P, Settimi L, Belli S, Magnani C, di Orio F. Mortality study on a cohort of Italian licensed pesticide users. Sci Total Environ. 1994 Jun 20;149(3):183-91. doi: 10.1016/0048-9697(94)90178-3. Ronco G, Costa G, Lynge E. Cancer risk among Danish and Italian farmers. Br J Ind Med. 1992 Apr;49(4):220-5. doi: 10.1136/oem.49.4.220. Forastiere F, Quercia A, Miceli M, Settimi L, Terenzoni B, Rapiti E, Faustini A, Borgia P, Cavariani F, Perucci CA. Cancer among farmers in central Italy. Scand J Work Environ Health. 1993 Dec;19(6):382-9. doi: 10.5271/sjweh.1458. Tables Table 1. Number of deaths. Blue-collar, low educated workers, Italy 2005-2018. Sector of employment Selection criteria Number of deaths All Registered in the INPS archive 1,634,187 All Registered in the INPS archive with permanence and prevalence of a working sector 1,210,709 Agriculture As above 64,200 Services As above 107,650 Table 2. Characteristics of study population included in the study, Italy, 2005-2018. Agriculture (cases) Services (controls) N (%) N (%) Gender Male 48,058 (75%) 55,871 (52%) Female 16,142 (25%) 51,779 (48%) Age of death (years) 1st Quartile 70 71 Median 77 80 3rd Quartile 82 86 Education Middle school diploma 9,599 (15%) 24,122 (22%) Primary school diploma 54,601 (85%) 83,528 (78%) Length of employment (years) 1st Quartile 27 3 Median 31 6 3rd Quartile 35 12 Year of end employment 1st Quartile 2001 1981 Median 2005 1986 3rd Quartile 2010 1994 Age at first employment (years) 1st Quartile 33 39 Median 39 46 3rd Quartile 44 52 * Number of individuals. Table 3 Mortality odds ratios (MOR) and 95% confidence intervals (CI) of mental, behavioral and neurodevelopmental disorders and nervous system diseases** in the agricultural sector (main analysis and by gender), Italy, 2005–2018. MORs are calculated in reference to the service sector. All Male Female Cause of death (ICD-10 codes) Cases (controls) Adjusted MOR (95%CI)* Cases (controls) Adjusted MOR (95%CI)* Cases (controls) Adjusted MOR (95%CI)* Mental, Behavioral and Neurodevelopmental disorders (F01-F99) 1,126 (63,599) 0.82 0.74 0.91 846 (47,212) 0.85 0.74 0.97 280 (15,862) 0.81 0.67 0.97 Vascular dementia (F01) 294 (63,906) 0.95 0.77 1.17 230 (47,828) 1.07 0.82 1.38 64 (16,078) 0.81 0.56 1.17 Unspecified dementia (F03) 716 (63,484) 0.89 0.77 1.02 528 (47,530) 0.85 0.71 1.02 188 (15,954) 0.86 0.68 1.09 Diseases of the nervous system (G00-G99) 2513 (61,687) 1.03 0.96 1.11 1805 (46,253) 1.04 0.95 1.14 708 (15,434) 0.99 0.87 1.11 Spinal muscular atrophy (SMA) (G12) 261 (63,939) 1.26 1.03 1.56 160 (47,898) 1.24 0.92 1.66 101 (16,041) 1.24 0.91 1.69 Parkinson's disease (G20) 742 (63458) 1.16 1.00 1.34 620 (47,438) 1.19 0.99 1.41 122 (16,020) 1.03 0.76 1.39 Alzheimer's disease (G30) 865 (63,335) 1.00 0.89 1.12 568 (47,490) 1.00 0.86 1.17 297 (15,845) 1.00 0.83 1.21 Other degenerative diseases of nervous system and disorders of brain (G31; G93) 208 (63,992) 0.86 0.68 1.07 157 (47,901) 0.89 0.67 1.19 51 (16,091) 0.78 0.52 1.16 Multiple sclerosis (G35) 19 (64,181) 0.42 0.23 0.78 7 (48,051) 0.39 0.14 1.10 12 (16,130) 0.55 0.25 1.20 Epilepsy (G40) 76 (64,124) 0.92 0.62 1.34 56 (48,002) 0.90 0.56 1.47 20 (16,122) 0.83 0.42 1.62 *Adjusted for or stratified by gender, age class, educational level, year of death, and region of residence. ** The reported causes of death are those that scientific literature has indicated as possible associations with occupational risks in agriculture. Table 4 Mortality odds ratios (MOR) and 95% confidence intervals (CI) of malignant neoplasms** of lymphoid, hematopoietic and related tissue in the agricultural sector (main analysis and by gender), Italy, 2005–2018. MORs are calculated in reference to the service sector. All Male Female Cause of death (ICD-10 codes) Cases (controls) Adjusted MOR (95%CI)* Cases (controls) Adjusted MOR (95%CI)* Cases (controls) Adjusted MOR (95%CI)* Malignant neoplasms of lymphoid, hematopoietic and related tissue (C81-C96) 2,222 (61,978) 1.30 1.21 1.40 1,566 (46,492) 1.33 1.21 1.47 656 (15,486) 1.19 1.05 1.36 Hodgkin lymphoma (C81) 47 (64,153) 1.10 0.65 1.86 28 (48,030) 0.77 0.40 1.47 19 (16,123) 1.89 0.77 4.68 Non-follicular lymphoma (C83) 82 (64,118) 1.59 1.03 2.46 60 (47,998) 1.98 1.11 3.54 22 (16,120) 1.06 0.50 2.25 Other specified and unspecified types of non-Hodgkin lymphoma (C85) 567 (63,682) 1.05 0.91 1.22 405 (47,653) 1.13 0.94 1.35 162 (15,980) 0.93 0.72 1.19 Malignant immunoproliferative diseases (C88) 30 (64,170) 3.21 1.52 6.78 20 (48,038) 1.99 0.82 4.80 10 (16,132) 7.58 1.87 30.67 Multiple myeloma (C90) 546 (63,654) 1.42 1.22 1.65 366 (47,692) 1.40 1.14 1.71 180 (15,962) 1.41 1.11 1.80 Lymphoid leukemia (C91) 272 (63,928) 1.35 1.09 1.67 204 (47,854) 1.37 1.05 1.79 68 (16,074) 1.20 0.82 1.76 Myeloid leukemia (C92) 474 (63,726) 1.36 1.16 1.60 330 (47,728) 1.48 1.20 1.82 144 (15,998) 1.08 0.82 1.43 Leukemia of unspecified cell (C95) 142 (64,058) 1.32 0.99 1.77 101 (47,957) 1.31 0.91 1.89 41 (16,101) 1.29 0.76 2.17 *Adjusted for or stratified by gender, age class, educational level, year of death, and region of residence. ** The reported causes of death are those that scientific literature has indicated as possible associations with occupational risks in agriculture. Table 5 Mortality odds ratios (MOR) and 95% confidence intervals (CI) of other cancer diseases** in the agricultural sector (main analysis and by gender), Italy, 2005–2018. MORs are calculated in reference to the service sector. All Male Female Cause of death (ICD-10 codes) Cases (controls) Adjusted MOR (95%CI)* Cases (controls) Adjusted MOR (95%CI)* Cases (controls) Adjusted MOR (95%CI)* Malignant neoplasm of Lip (C00) 16 (64,184) 2.76 0.91 8.36 14 (48,044) 2.96 0.91 9.67 2 (16,140) NC Malignant neoplasm of pharynx (C09-C10, C12-C14) 154 (64,046) 0.83 0.64 1.08 140 (47,918) 0.90 0.67 1.19 14 (16,128) 0.53 0.25 1.11 Malignant neoplasm of nasopharynx (C11) 16 (64,184) 1.09 0.52 2.32 14 (48,044) 1.50 0.63 3.57 2 (16,140) 0.59 0.11 3.10 Malignant neoplasm of stomach (C16) 1,732 (62,468) 1.30 1.20 1.41 1,291 (46,767) 1.32 1.19 1.47 441 (15,701) 1.18 1.01 1.37 Malignant neoplasm of colon-rectum (C18-C21) 2,815 (61,385) 1.24 1.16 1.32 2,068 (45,990) 1.26 1.16 1.37 747 (15,395) 1.09 0.97 1.23 Malignant neoplasm of liver and intrahepatic bile ducts (C22) 1,259 (62,941) 0.77 0.71 0.85 994 (47,064) 0.78 0.71 0.87 265 (15,877) 0.83 0.68 1.00 Malignant neoplasm of sinuses and nasal cavity (C30.0, C31) 25 (64,175) 1.03 0.51 2.10 17 (48,041) 0.78 0.33 1.85 8 (16,134) 2.07 0.60 7.17 Malignant neoplasm of larynx (C32) 217 (63,983) 0.80 0.65 1.00 198 (47,860) 0.78 0.62 0.97 19 (16,123) 1.32 0.63 2.76 Malignant neoplasm of trachea, bronchus and lung (C33, C34) 4,201 (59,999) 0.75 0.71 0.78 3,623 (44,435) 0.76 0.72 0.81 578 (15,564) 0.63 0.56 0.72 Melanoma of skin (C43) 280 (63,920) 1.38 1.12 1.69 188 (47,870) 1.44 1.10 1.88 92 (16,050) 1.14 0.81 1.61 Malignant neoplasm of connective and soft tissue (C49) 110 (64,090) 1.37 0.99 1.89 75 (47,983) 1.56 1.02 2.39 35 (16,107) 0.92 0.53 1.62 Malignant neoplasm of breast (C50) 1,014 (63,186) 1.02 0.92 1.13 25 (48,033) 1.47 0.73 2.95 989 (15,153) 1.01 0.91 1.11 Malignant neoplasm of ovary (C56) 421 (63,779) 0.83 0.71 0.97 421(63,779) 0.83 0.71 0.97 Malignant neoplasm of prostate (C61) 1,582 (62,618) 2.03 1.85 2.24 1582 (62,618) 2.03 1.85 2.24 Malignant neoplasm of kidney (C64-C66, C68) 724 (63,476) 1.28 1.12 1.47 575 (47,483) 1.28 1.09 1.50 149 (15,993) 1.31 1.00 1.71 Malignant neoplasm of bladder (C67) 672 (63,528) 0.86 0.76 0.97 603 (47,455) 0.83 0.72 0.95 69 (16,073) 1.05 0.74 1.48 Malignant neoplasm of brain and central nervous system (C71, C72) 601 (63,599) 1.30 1.13 1.50 376 (47,682) 1.40 1.16 1.69 225 (15,917) 1.19 0.95 1.48 *Adjusted for or stratified by gender, age class, educational level, year of death, and region of residence. NC, not calculated ** The reported causes of death are those that scientific literature has indicated as possible associations with occupational risks in agriculture. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Apr, 2024 Submission checks completed at journal 16 Apr, 2024 Editor assigned by journal 16 Apr, 2024 First submitted to journal 15 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4268499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291694658,"identity":"6756fbeb-4b24-4e16-a1ae-e6070e48f34a","order_by":0,"name":"Claudio Gariazzo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIie2QMQoCMRBFRwaSZiRtFkSvMKU2ehXBdgXBRlA0ILjNYu0x9ggrgbXRwtJO8AQiCIKIG0uLNaVFXjHVvP+HAQgE/hZ2A/McQDcA/RXRdwoB+jkO+ogEv2qUkcfbZNTttZL0akezNoFUeaWicxpHex5gLT1kdlN4HMZA/cgwCtTDzJLwUwYPw4tyOT5benkpsihbrCYdg62vPBRdHtMxvGNNBdv6WpNA5EpFJcnlZJ7T8mPLy43u86ZS23N1DdJXpqjed8gfmYFAIBB4A2mpNE7g6gpZAAAAAElFTkSuQmCC","orcid":"","institution":"Italian Workers’ Compensation Authority (INAIL)","correspondingAuthor":true,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Gariazzo","suffix":""},{"id":291694659,"identity":"e8b30573-4986-4f8d-9d27-09bf0411cc14","order_by":1,"name":"Alessandro Marinaccio","email":"","orcid":"","institution":"Italian Workers’ Compensation Authority (INAIL)","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Marinaccio","suffix":""},{"id":291694661,"identity":"5f51fa8a-3f95-4249-94dc-14dc441e7588","order_by":2,"name":"Dario Consonni","email":"","orcid":"","institution":"Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Dario","middleName":"","lastName":"Consonni","suffix":""},{"id":291694663,"identity":"1d994798-86fe-44b3-b18b-269cf6f5f18c","order_by":3,"name":"Lucia Miligi","email":"","orcid":"","institution":"Fondazione ISPRO Institute for Cancer Research, Prevention and Clinical Network","correspondingAuthor":false,"prefix":"","firstName":"Lucia","middleName":"","lastName":"Miligi","suffix":""},{"id":291694664,"identity":"de78153c-106b-41f4-8ec1-4cf25e11b275","order_by":4,"name":"Stefania Massari","email":"","orcid":"","institution":"Italian Workers’ Compensation Authority (INAIL)","correspondingAuthor":false,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Massari","suffix":""}],"badges":[],"createdAt":"2024-04-15 09:01:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4268499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4268499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55321266,"identity":"41fc8e6d-cabd-4d57-ac63-eb5503adef43","added_by":"auto","created_at":"2024-04-25 16:21:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54689,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of selection of the population under study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4268499/v1/97e1be26f22eff52dac8349d.png"},{"id":55322702,"identity":"61b19646-2651-4ba8-ab63-a7b9503b8993","added_by":"auto","created_at":"2024-04-25 16:29:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":531030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4268499/v1/7939f0b2-8175-4c20-bed2-8574ae681db3.pdf"},{"id":55321268,"identity":"7fb7c230-1715-4e0c-bd07-89bc21b9ee02","added_by":"auto","created_at":"2024-04-25 16:21:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29489,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4268499/v1/8cdc0ab7e39d3db6eac732a3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mortality from nervous system diseases and cancer in agriculture workers: a case-control study in Italy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWork in agriculture includes different job activities such as cultivation, harvesting of crops, rearing animals, and forestry. According to World Bank data, based on International Labour Organization modeled estimates, from year 1991 to 2021 the global workforce employed in agriculture passed from 43 to 26% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], mainly driven by the migration of population from countryside to cities, changes in the general economy, cultivation equipment and products market. A similar trend was observed in Italy. According to data provided by the National Statistical Institute (ISTAT), about 3M of workers were employed in agriculture in 1982 with a continuous decreasing trend leading to the about 1M in 2004 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The number of farms reduced from 3.5M to 1.5M from 1961 to 2010, in agreement with the reduction of the cultivated surface from 26M to 17M hectares [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the different occupational or environmental risk factors for those working or living in agriculture settings, pesticides are the most important agents. Additional agents are solar radiation, engine exhausts, solvents, dusts, and zoonotic microbes. The use of pesticides has been ruled by European directives (91/414/ECC and 2009/128/EC) concerning the placing of plant protection products on the market, and a framework for Community action to achieve the sustainable use of pesticides. Pesticides are a broad category, which include insecticides, fungicides, herbicides, plant growth regulators, and other functional categories, with different levels of toxicity determined by the active ingredients and other agents in the composition of commercial products. The use of plant protection products for agricultural use in Italy has decreased from 18,000 tons in 1997 to 5,000 tons in 2018, especially for those with very toxic contents [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, rural and greenhouse workers, those working in pesticide manufacture, mixing or application, may still have significant pesticides exposure, in addition to that experienced in the past. Also bystanders may be exposed living in proximity of cultivated crop. Moreover, the presence of pesticide residues in water, in foodstuff and in the environment near agriculture farms, involves the general population with a significant background exposure to these compounds.\u003c/p\u003e \u003cp\u003eThe health effects due to their exposure are well known in literature. The most studied health effect is cancer. The early reviews showed that farmers have a lower risk of most major causes of death than the general population particularly in terms of total mortality, total cancer, health diseases and specific cancers like lung cancer [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, according to cohort and case-control studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], this occupational group has a higher risk for certain types of cancers, particularly Soft Tissue Sarcomas (STSs), non-Hodgkin lymphomas (NHLs) [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], Hodgkin\u0026rsquo;s disease (HD), leukemia, multiple myeloma (MM) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], prostate cancer [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and cancer of the skin and lip [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Italy several multicenter case-control studies have investigated the association between agricultural work, pesticides exposure and risk of cancer [19\u0026ndash;23; 10, 11, 14]. Others examined the association between cancer and the exposure to pesticides of licensed users [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe potential of pesticides as human carcinogens has been highlighted by international agencies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, not all studies agreed on the association between agricultural work and cancer-specific outcomes, depending on the type of study, study area, workers involved, job-related activities, gender- or age-based differences, available exposure data and working histories.\u003c/p\u003e \u003cp\u003eSome studies have suggested that pesticides may cause other types of cancer, such as brain cancer [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but others not [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the recent and past literature, exposure to agriculture agents like pesticides has also been associated with neurodegenerative diseases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Parkinson\u0026rsquo;s disease, amyotrophic lateral sclerosis (ALS) and Alzheimer\u0026rsquo;s disease are the most common neurodegenerative disorders, that has been related with exposure to pesticide with an increase of risk by at least 50% [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. More recent studies involved other health effects such as epilepsy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], tremor [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and neurobehavioral, neuromotor, and neurocognitive effects [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Not all studies agreed on the association between agricultural work and increased risk of brain disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Misclassification and inadequate response rates are considered as possible explanations for the non-association observed in some studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, the scientific evidence suggests an association between neoplastic and neurological effects in farmers exposed to pesticides and other agents used in agriculture, the question related to the exposure assessment became increasingly complex and need further studies to better understand some aspects of the topic and the association of past exposure with health outcomes. There is a lack of a comprehensive assessment of occupational risks in agriculture, as most studies focus on specific regions of a country with a limited population enrolled, primarily in cohort studies, as well as on neurodevelopmental disorders and diseases of the nervous system other than Parkinson\u0026rsquo;s. This study aims to fulfill this lack of information by using a case-control study design, based on national registered mortality data in the period 2005\u0026ndash;2018 integrated with working histories derived from the National Social Insurance archive.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe used a case-control study based on countrywide mortality data covering the period 2005\u0026ndash;2018 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. We selected as cases different causes of death likely associated to main occupational risk factors in agriculture including neuro-degenerative and malignant neoplasm. For each cause of death, we selected as controls the causes of death out of the one under study. We defined deceased workers who had worked in the agricultural sector as exposed, while those who had worked in the service sector were considered unexposed, since assumed to be representative of the general unexposed population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMortality data\u003c/h2\u003e \u003cp\u003eMortality data from year 2005 to 2018, coded according to the tenth International Classification of Diseases (ICD-10) were retrieved from ISTAT. We selected the following groups of causes of death of interest: mental, behavioral and neurodevelopmental disorders (F01-F99); diseases of the nervous system (G00-G99); and malignant neoplasms(C00-C96). Among these groups, we selected a few specific diseases for their particular interest in this field of study. A detailed list of diseases is shown in Tables \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMortality data included information on gender, age of death, which was classified in 15 classes (\u0026lt;\u0026thinsp;19, 20\u0026ndash;84 in 5 years and 85+), educational level (2 classes: middle and primary school), and date and location of death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOccupational data\u003c/h2\u003e \u003cp\u003eWe used the information on working history retrieved from the National Social Insurance Agency (INPS). The data consists of about 55% of the Italian workforce and do not include data on public employment, self-employment, artisans, domestic workers, para-subordinate workers, and occasional workers. Data consist of employment periods from 1974 onwards spent by the worker in each private sector in his/her working-life [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Information about the specific occupations was not available except for a blue-collar indication. Economic sectors in INPS files are classified according the ATECO 81. We classified economic activities according to the Statistical Classification of Economic Activities in the European Community, NACE Rev. 2, grouped into broader categories data [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] for a total of 48 sectors of employment. Among them, we selected those deaths related to persons who worked in the agricultural and in the service sectors. The latter is used as reference population and includes wholesale and retail trade, accommodation and food service activities, financial and insurance activities, administrative and support service activities.\u003c/p\u003e \u003cp\u003eThe occupational data were linked at individual level with the mortality ones using the tax identification code by authorized personnel. The linked dataset was then anonymized, stored, and processed under strict controls to protect personal data.\u003c/p\u003e \u003cp\u003eTo maximize the etiological link between causes of death and potential occupational exposures, we carried out a number of data selection procedures. First, for both cases and controls we selected blue-collar workers with low educational level (primary or middle school), because they are more likely to have been engaged in jobs and tasks entailing exposure to various agents in agriculture. Second, we calculated the median value of the years of employment for each economical sector and only those cases with a duration of employment higher than that value (permanence criteria) were selected. Third, in order to avoid that multiple sectors of employment could confound the relationship between the cause of death and the specific sector, we selected cases in which the length of employment in a specific sector was prevalent (defined as the sector with at least 75% of the total years of registered working activities).\u003c/p\u003e \u003cp\u003eIn summary, only cases and controls with a blue-collar definition, low educated and with a sufficient (permanence criteria) and prevalent length of employment in a specific sector were included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flow chart of process of selecting the study population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eWe fitted logistic regression models to calculate mortality odds ratios (MORs) and 95% confidence intervals (CI) for various causes of death. Occupational exposure was modelled as \u0026ldquo;ever/never been employed\u0026rdquo; in which workers employed in the agriculture were considered as exposed, and those ones in the services as unexposed (reference). The models were adjusted for gender, age class, educational level, year of death and region of residence, the latter included to consider the heterogeneities of cultivations in the national territory. We also performed analyses stratified by gender and length of employment 0\u0026ndash;30; 30\u0026thinsp;+\u0026thinsp;years. For simplicity we did not perform lagged analyses because we examined a large spectrum of outcomes, each requiring a different lag.\u003c/p\u003e \u003cp\u003eFinally, a sensitivity analysis was carried out to investigate about the impact of selecting the service sector as reference. The main analysis was repeated using all other sectors (excluding the agriculture one) as a reference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 1.6M of blue-collar workers deaths during the years 2005\u0026ndash;2018, about 64K and 107K worked in the agriculture and service sectors with a sufficient (permanence criteria) and prevalent length of employment, respectively (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Most of those who worked in agriculture were men (75%) with a primary school diploma (85%) and started working at a median age of 39 and continued to work for a median of 31 years (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This result is consistent with the start date of working history (1974) and with the tendency of farmers in beginning work at early age and ending it very late. The control group (workers in the service sector) were balanced in terms of gender (52% vs 48%), but with higher age at first employment (46 as median value) and with shorter length of employment (6 years as median value) than those who worked in the agriculture sector (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMost of the workers were employed in agriculture from 1974 to 2004 (table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e of supplementary materials (SM)). The number of agricultural workers by period of working history and disease of death is shown in table S3 of SM.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows MORs results for mental, behavioural and neurodevelopmental disorders and nervous system diseases in workers employed in the agricultural sector compared to those in services. We found positive associations between employment in agriculture and spinal muscular atrophy (SMA) and Parkinson\u0026apos;s diseases (in men only).\u003c/p\u003e\n\u003cp\u003eMost malignant neoplasms of lymphoid, hematopoietic and related tissue were in excess in those ever employed in the agricultural sector, particularly in men (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Malignant immunoproliferative diseases emerged in women only but based on a few cases. Multiple myeloma and lymphoid or unspecified leukemias were in excess in both genders.\u003c/p\u003e\n\u003cp\u003eWe found positive associations between employment in agriculture and mortality from several other cancers, including cancer of lip, stomach, skin melanoma, colon-rectum, connective and soft tissue, prostate, kidney, brain and central nervous system (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Reduced risks were found for many causes of death, including multiple sclerosis, malignant neoplasms of liver, larynx, lung, ovary and bladder (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Some elevated risks emerged only in men (lip cancer, melanoma of skin, malignant neoplasm of connective and soft tissue and of brain and central nervous system) Cancers of stomach, colon-rectum, and kidney were positively associated with work in agriculture in both genders, although with MORs of different magnitude.\u003c/p\u003e\n\u003cp\u003eFor malignant neoplasms of brain, skin, stomach, colon-rectum, kidney, and prostate we observed positive trends of risk with the length of employment (table S3 of SM). Conversely, risks of lip cancer, multiple myeloma, myeloid or unspecified leukemia, cancer of ovary, Parkinson\u0026apos;s disease, and spinal muscular atrophy showed lower risks for those who worked\u0026thinsp;\u0026lt;\u0026thinsp;30 years in agriculture.\u003c/p\u003e\n\u003cp\u003eThe sensitivity analyses about the relative importance of the reference sector shows that the pattern of identified risks did not in general change when all other occupational sectors are used as reference (table S4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide study, we analyzed the causes of death of more than 64k workers employed in the agriculture in comparison with a control group of 107K workers of the service sector. We found agricultural workers to be associated with mortality for neurological and malignant cancer-specific diseases. In particular, we identified in the group of neurological diseases, associations with mortality for spinal muscular atrophy and Parkinson\u0026rsquo;s disease, while in the lymphoid, hematopoietic and related tissue cancer group, we found associations with lymphoma, myeloma and leukemia diseases. In the malignant solid neoplasm group, we also identified positive associations in agricultural workers with mortality for cancer in stomach, colon-rectum, skin, connective and soft tissue, prostate, kidney and brain and central nervous system. An evident tendency towards an increased risk for cancer of lip was found for men only, likely caused by exposure to solar radiation.\u003c/p\u003e \u003cp\u003ePesticides are considered the main agent of exposure for agricultural workers. They are specifically manufactured and used to eradicate or control undesired organisms. Insecticides, herbicides, fungicides, nematocides, rodenticides and other preparations are the pesticides for which there is a higher concern for occupational health [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Best-known toxic mechanisms of pesticides are: interference with axonal nerve conduction; synaptic transmission; mitochondrial respiration; steroid biosynthesis; blood coagulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The main target organs are the skin, the eyes, the mucosae of the respiratory tract, the digestive apparatus, and the nervous system. Other chronic effects involve liver and kidney toxicity, as well as chronic effects on the skin, on immune, respiratory, and endocrine systems, on blood, and on peripheral and central nervous system [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of this study are mostly in line with the ILO\u0026rsquo;s list of occupational diseases caused by pesticides exposure, and with the past and recent literature in this field [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs for the diseases of the nervous system, we found farmers, and male in particular, to be at risk for Parkinson\u0026rsquo;s diseases. The evidence of the association between working in agriculture and Parkinson\u0026rsquo;s diseases is large but also controversial. In a review by Nandipati and Litvan [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the authors addressed that among the different pesticides, rotenone, paraquat, and organochlorines have been well-documented in human epidemiological studies to be associated with Parkinson\u0026rsquo;s diseases, while organophosphates, pyrethroids, and polychlorinated biphenyls require further study. In a more recent systematic review, Sturm et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] examined 22 studies about the relationship between agricultural work and Parkinson\u0026rsquo;s disease or Parkinsonism but they did not all agree on the presence of an association. Positive associations were found in studies carried out in France [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], Norway [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], US [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and Spain [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In Italy one study concerning farming and Parkinson\u0026rsquo;s disease found no association [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Positive association was instead found in a case-control study in north-east of Italy [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. An important genetic influence seems to be a factor [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study found an association between working in agriculture and mortality for the group of spinal muscular atrophy diseases (SMA) without gender difference. A few studies found an association between pesticide exposure and the development of ALS, a disease part of SMA group [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In Italy there is poor information about this disease in connection with working in agriculture. Filippini et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] in a population-based case-control study in four Italian provinces, found no statistically significant positive association with ALS risk in agricultural workers. The results of our study, are in line with the scientific literature and add further evidence to this field of study. However, when stratified by sex this association loss of statistical significance.\u003c/p\u003e \u003cp\u003eWe did not find associations with mortality for Alzheimer\u0026rsquo;s disease, multiple sclerosis, and epilepsy, although these health effects were positively associated with exposure to pesticides in Spain [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and in a review by Gangemi et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. At the same time, we did not find any association with mental, behavioral and neurodevelopmental disorders as a whole, and in vascular and unspecified dementia. One possible reason was the use of mortality data instead of hospitalization data of these diseases, which could have masked the associations.\u003c/p\u003e \u003cp\u003eAs for cancer effects caused by long-term exposure to agents during working in agriculture, some studies have suggested an association between pesticide exposure and Hodgkin's disease, leukaemia, non-Hodgkin lymphoma, multiple myeloma (5,6, 10\u0026ndash;14) as well as tumors of the lip, stomach, prostate, skin, brain, and connective tissues [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The International Agency for Research on Cancer (IARC), evaluating the cancer risk associated with non-arsenical insecticide spraying, has concluded that this activity is probably carcinogenic to humans (Group 2A) and recently some active ingredients have been evaluated as certain or probable carcinogens for humans [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], likely causing an increased risk for skin, lymphatic, and lung cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. However, no pesticide has sufficient evidence in humans to be classified as a brain or central nervous system carcinogen by IARC [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of our study confirm the above evidence of cancer effects related to agricultural workers, providing evidence of risk for specific cancer types.\u003c/p\u003e \u003cp\u003eAs for brain cancer, our results are in line with a study about mortality in male farmers licensed to use pesticides [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], with an Italian case-control study on farmer work and the cancer morbidity [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and with some recent reviews [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our study provides additional information about its prevalence in men workers, and in farmers who worked for more than 30 years.\u003c/p\u003e \u003cp\u003eIn Italy both cohort and case-control studies were conducted to investigate cancer effects among agricultural workers.\u003c/p\u003e \u003cp\u003eFaustini et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] carried out a cohort-based mortality study of farm workers, in which no statistically significant excesses were observed in cancer mortality. A cohort of licensed pesticide users was investigated by Fig\u0026agrave;-Talamanca et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] resulting in a standardized mortality ratio (SMR) of 72 for all cancers. An increase of risk for melanomas and eye tumors were observed in a cohort of farmers and for lymphoma and tumors of the connective tissue in the sub cohort of subjects living in villages with mainly arable land [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy comparing cancer risk mortality among two cohorts of Danish and Italian farmers, Ronco et al. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] found a consistent risk reduction for cancer of the lung, bladder, small intestine, colon, rectum, and prostate, but an excess risk for leukemia in female only, and a slight excess risk for non-Hodgkin's lymphoma.\u003c/p\u003e \u003cp\u003eAmong the case-control studies, an early mortality study about farmers in central Italy found no association with NHL, Hodgkin's lymphoma, multiple myeloma, and leukemia but when the different crops are considered some increasing risks are observed [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The findings of other Italian studies were more in agreement with our results about lymphoid, hematopoietic and related tissue cancer group. Among them a study carried out in a north area of Italy found increasing risk for NHL and chronic lymphocytic leukemia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A multicenter case-control study suggested an increased risk for NHL and leukemia, and some chemical classes of pesticides, although few are statistically significant and some are based on few exposed cases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As in our study, no statistically significant increased risk of NHL was found in a population-based case-control study carried out in Italy and involving many agricultural and mixed areas, in which exposure in agriculture was assessed by the use of a specific questionnaire given to subjects who were exposed to phenoxy herbicides not using protective equipment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A hospital-based case-control study was conducted in five Italian rural areas by Settimi et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which investigated the association between cancer and farming among male agricultural workers. They found increased risks of cancer associated with agricultural workers for stomach, rectum, larynx and prostate, in agreement with the findings of this study out of the larynx one.\u003c/p\u003e \u003cp\u003eThe above Italian studies show a large spectrum of specific positive associations results, often with inconsistencies and disagreement likely due to specific peculiarities of each study. Among them we could mention: type of study (eg. cohort and case-control), peculiarities in the sample of farmers (eg. population, pesticide licensed users), studied area (cohort limited to specific Provinces, multicenter) and their representativeness at national level, different job activities (eg. cultivation, harvesting of crops), methods of farmer identification (questionnaire, registries, census), exposure assessment and heterogeneities of cultivations at regional level. It's worth noting that the healthy worker effect or, the healthier lifestyle of farm families, can be easily observed for agricultural workers and can lead to an attenuation of the risk of death.\u003c/p\u003e \u003cp\u003eAlthough this study provides new and additional associations between health effects and agricultural workers in Italy, it contains few limitations that must be considered. The first is that occupational administrative data cannot adequately characterize occupational exposure profiles. INPS data report the industrial sector but do not contain information on workers\u0026rsquo; roles in a specific sector or exposure variables such as exposure intensity. In addition, the complexity of exposure pattern, the difficulty in reconstructing past exposure, considering the change over time in specific chemical use, and the variation in work practices, should be taken into account. Consequently, occupation data of the present study only concern about the working-time spent in specific sectors, which might be involved with exposure to agents possibly causing diseases. Therefore, we used a rather broad classification of agricultural workers, not considering important factors for the exposure assessment like: specific job-related activities; heterogeneity of agricultural work itself (eg. crop type, animal type); type of pesticide applied; complex synergies among risks; and actual dose of exposure. We also not considered individual factors like genetic, lifestyles, socioeconomic status and co-morbidities, as well as external factors like environmental differences. We could not account for latency of the specific outcomes related to the exposure agents due to lack of information.\u003c/p\u003e \u003cp\u003eNevertheless, this study provides a national picture of neurological and cancer risks that could be experienced by workers in agriculture in Italy, overcoming the limited information and representativeness provided by former case-control and cohort studies. Performing a record linkage of administrative data, and in particular between INPS files and mortality data, allowed us to fill the gap of the misclassification of the occupational information derived by questionnaire or census. To enhance the etiological relationship between the sector of employment and health outcome, we adopt a selection criterion based on a prevalent sector of employment, and a sector specific duration of it. Moreover, it was possible to establish an epidemiological study with high statistical power and to study the excess mortality risks among men and women separately.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWorking in agriculture for long term was demonstrated to produce health risks for workers likely due to occupational and environmental exposure to pesticides and other agents here located. Although their use has been quantitative and qualitative limited, their health effects remain of concern for their large use in the past. Despite the limits of this study, it found similar increase of risks for specific diseases, part of them already identified by several studies. The findings of this study demand for further focused limitation measures, especially for those belonging to classes of compounds for which there are some specific risks for human health. Possible actions should be oriented to the substitution of toxic and persistent active substances with less active, more selective and less persistent ones.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate: \u0026nbsp;\u003c/strong\u003eNot applicable due to the use of administrative data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Data access was granted by the National Statistics Program (PSN 2020-2022), which provides a prior authorization with the force of law of the Italian Data Protection Authority for statistical works of public interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The authors do not have permission to share data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone Declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Institute for the Insurance of Occupational Injuries (INAIL) in the framework of the 2022\u0026ndash;2024 INAIL Scientific Research Plan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eClaudio Gariazzo: Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Writing - Review \u0026amp; Editing; Stefania Massari: Conceptualization, Data Curation; Methodology, Formal analysis, Writing - Review \u0026amp; Editing; Dario Consonni: Methodology, Writing - Review \u0026amp; Editing; Lucia Miligi: \u0026nbsp;Writing - Review \u0026amp; Editing; Alessandro Marinaccio: Conceptualization, Methodology, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: Access to data was granted by the fact that this activity was included in the National Statistics Program (PSN 2017\u0026ndash;2019) which provides a prior authorization with the force of law of the Italian Data Protection Authority for statistical surveys of public interest. All epidemiological analyses were performed using anonymous data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWord Bank 2021. Employment in Agriculture (% of Total Employment) (Modeled ILO Estimate)|Data. Available online: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS (accessed on 14 December 2023).\u003c/li\u003e\n\u003cli\u003eIstat, 2023a, https://www.istat.it/it/censimenti/censimenti-precedenti/agricoltura. \u003c/li\u003e\n\u003cli\u003eIstat, 2023b, Censimenti generali dell\u0026apos;agricoltura. https://seriestoriche.istat.it/index.php?id=1\u0026amp;no_cache=1\u0026amp;tx_usercento_centofe%5Bcategoria%5D=13\u0026amp;tx_usercento_centofe%5Baction%5D=show\u0026amp;tx_usercento_\u003cbr\u003ecentofe%5Bcontroller%5D=Categoria\u0026amp;cHash=e3503d8195dd4231ff53ba078ad5c124\u003c/li\u003e\n\u003cli\u003eISTAT, 2023c. http://dati.istat.it/Index.aspx?DataSetCode=DCSP_FITOSANITARI#\u003c/li\u003e\n\u003cli\u003eBlair A, Malker H, Cantor KP, Burmeister L, Wiklund K. Cancer among farmers. A review. Scand J Work Environ Health. 1985 Dec; 11(6):397-407. doi: 10.5271/sjweh.2208.\u003c/li\u003e\n\u003cli\u003eBlair A and Zahm S H. Agricultural Exposures and Cancer. Environ Health Perspect 103(Suppl 8):205-208 (1995)\u003c/li\u003e\n\u003cli\u003eAcquavella J., Olsen G., Cole P., Ireland B., Kaneene J., Schuman S., Holden L. Cancer among farmers: A meta-analysis. Annals of Epidemiology, 1998,8 (1), 64 \u0026ndash; 74. DOI: 10.1016/S1047-2797(97)00120-8.\u003c/li\u003e\n\u003cli\u003eLerro, C.C., Koutros, S., Andreotti, G. et al. Cancer incidence in the Agricultural Health Study after 20 years of follow-up. Cancer Causes Control 30, 311\u0026ndash;322 (2019). https://doi.org/10.1007/s10552-019-01140-y.\u003c/li\u003e\n\u003cli\u003eTogawa K, Leon ME, Lebailly P, Beane Freeman LE, Nordby KC, Baldi I, MacFarlane E, Shin A, Park S, Greenlee RT, Sigsgaard T, Basinas I, Hofmann JN, Kjaerheim K, Douwes J, Denholm R, Ferro G, Sim MR, Kromhout H, Sch\u0026uuml;z J. Cancer incidence in agricultural workers: Findings from an international consortium of agricultural cohort studies (AGRICOH). Environ Int. 2021 Dec;157:106825. doi: 10.1016/j.envint.2021.106825.\u003c/li\u003e\n\u003cli\u003eDe Roos AJ, Schinasi LH, Miligi L, et al. Occupational insecticide exposure and risk of non-Hodgkin lymphoma: A pooled case-control study from the InterLymph Consortium. Int. J. Cancer. 2021; 149(10): 1768-1786. https://doi.org/10.1002/ijc.33740.\u003c/li\u003e\n\u003cli\u003eDe Roos AJ, Fritschi L, Ward MH, et al. Herbicide use in farming and other jobs in relation to non-Hodgkin\u0026rsquo;s lymphoma (NHL) risk. Occupational and Environmental Medicine 2022;79:795-806.\u003c/li\u003e\n\u003cli\u003eLeon, M.E., Schinasi L. H., Lebailly P., et al. Pesticide use and risk of non-Hodgkin lymphoid malignancies in agricultural cohorts from France, Norway and the USA: a pooled analysis from the AGRICOH consortium, International Journal of Epidemiology, Volume 48, Issue 5, October 2019, Pages 1519\u0026ndash;1535, https://doi.org/10.1093/ije/dyz017\u003c/li\u003e\n\u003cli\u003eSchinasi, L.; Leon, M.E. Non-Hodgkin Lymphoma and Occupational Exposure to Agricultural Pesticide Chemical Groups and Active Ingredients: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2014, 11, 4449-4527. https://doi.org/10.3390/ijerph110404449.\u003c/li\u003e\n\u003cli\u003ePerrotta C, Kleefeld S, Staines A, Tewari P, De Roos AJ, Baris D, Birmann B, Chiu B, Cozen W, Becker N, Foretova L, Maynadi\u0026eacute; M, Nieters A, de Sanjos\u0026eacute; S, Miligi L, Seniori Costantini A, Purdue M, Spinelli J, Cocco P. Multiple myeloma and occupation: a pooled analysis by the International Multiple Myeloma Consortium. Cancer Epidemiol. 2013 Jun;37(3):300-5. doi: 10.1016/j.canep.2013.01.008.\u003c/li\u003e\n\u003cli\u003ePardo LA, Beane Freeman LE, Lerro CC, Andreotti G, Hofmann JN, Parks CG, Sandler DP, Lubin JH, Blair A, Koutros S. Pesticide exposure and risk of aggressive prostate cancer among private pesticide applicators. Environ Health. 2020 Mar 5;19(1):30. doi: 10.1186/s12940-020-00583-0.\u003c/li\u003e\n\u003cli\u003eKoutros, S., Beane Freeman, L.E., Lubin, J.H. et al. Risk of total and aggressive prostate cancer and pesticide use in the Agricultural Health Study. American Journal of Epidemiology, 2013, 177(1), pp. 59\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eKrestv S, Knutsson A. Occupational Risk Factors for Prostate Cancer: A Meta-analysis. J Cancer Prev. 2019 Jun;24(2):91-111. doi: 10.15430/JCP.2019.24.2.91.\u003c/li\u003e\n\u003cli\u003eLewis-Mikhael AM, Bueno-Cavanillas A, Ofir Giron T, Olmedo-Requena R, Delgado-Rodr\u0026iacute;guez M, Jim\u0026eacute;nez-Mole\u0026oacute;n JJ. Occupational exposure to pesticides and prostate cancer: a systematic review and meta-analysis. Occup Environ Med. 2016 Feb;73(2):134-44. doi: 10.1136/oemed-2014-102692.\u003c/li\u003e\n\u003cli\u003eNanni O, Amadori D, Lugaresi C, Falcini F, Scarpi E, Saragoni A, Buiatti E. Chronic lymphocytic leukaemias and non-Hodgkin\u0026apos;s lymphomas by histological type in farming-animal breeding workers: a population case-control study based on a priori exposure matrices. Occup Environ Med. 1996 Oct;53(10):652-7. doi: 10.1136/oem.53.10.652.\u003c/li\u003e\n\u003cli\u003eSettimi 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 Health. 2001;14(4):339-47.\u003c/li\u003e\n\u003cli\u003eMiligi, L., Costantini, A.S., Bolejack, V., Veraldi, A., Benvenuti, A., Nanni, O., Ramazzotti, V., Tumino, R., Stagnaro, E., Rodella, S., Fontana, A., Vindigni, C. and Vineis, P. (2003), Non-Hodgkin\u0026apos;s lymphoma, leukemia, and exposures in agriculture: Results from the Italian multicenter case-control study. Am. J. Ind. Med., 44: 627-636. https://doi.org/10.1002/ajim.10289\u003c/li\u003e\n\u003cli\u003eCocco P, Satta G, D\u0026apos;Andrea I, Nonne T, Udas G, Zucca M, Mannetje A\u0026apos;, Becker N, Sanjos\u0026eacute; Sd, Foretova L, Staines A, Maynadi\u0026eacute; M, Nieters A, Brennan P, Ennas MG, Boffetta P. Lymphoma risk in livestock farmers: results of the Epilymph study. Int J Cancer. 2013 Jun 1;132(11):2613-8. doi: 10.1002/ijc.27908.\u003c/li\u003e\n\u003cli\u003eMiligi L, Costantini AS, Veraldi A, Benvenuti A; WILL; Vineis P. Cancer and pesticides: an overview and some results of the Italian multicenter case-control study on hematolymphopoietic malignancies. Ann N Y Acad Sci. 2006 Sep;1076:366-77. doi: 10.1196/annals.1371.036.\u003c/li\u003e\n\u003cli\u003eAlberghini V, Luberto F, Gobba F, Morelli C, Gori E, Tomesani N. Mortality among male farmers licensed to use pesticides. Med Lav. 1991 Jan-Feb;82(1):18-24. PMID: 1865842.\u003c/li\u003e\n\u003cli\u003eFiga-Talamanca, Mearelli I., Valente P., Bascherini S., Cancer Mortality in a Cohort of Rural Licensed Pesticide Users in the Province of Rome, International Journal of Epidemiology, Volume 22, Issue 4, August 1993, Pages 579\u0026ndash;583, https://doi.org/10.1093/ije/22.4.579\u003c/li\u003e\n\u003cli\u003eSperati A, Rapiti E, Settimi L, Quercia A, Terenzoni B, Forastiere F. Mortality among male licensed pesticide users and their wives. Am J Ind Med. 1999 Jul;36(1):142-6. doi: 10.1002/(sici)1097-0274(199907)36:1\u0026lt;142::aid-ajim20\u0026gt;3.0.co;2-e. PMID: 10361599.\u003c/li\u003e\n\u003cli\u003eInternational Agency for Research on Cancer. Some Organophosphate Insecticides and Herbicides; IARC Monographs on the Evaluation of Carcinogenic Risks to Humans Vol. 112; IARC, World Health Organization: Lyon, France, 2017, ISBN 978-92-832-0178-6.\u003c/li\u003e\n\u003cli\u003eInternational Labour Organization (ILO). Diagnostic and exposure criteria for occupational diseases \u0026ndash; Guidance notes for diagnosis and prevention of the diseases in the ILO List of Occupational Diseases (revised 2010). ISBN 978-92-2-035683-8, 2022. Available at: https://www.ilo.org/global/topics/geip/WCMS_836362/lang--en/index.htm\u003c/li\u003e\n\u003cli\u003eKhuder, S.A., Mutgi, A.B. and Schaub, E.A. (1998), Meta-analyses of brain cancer and farming. Am. J. Ind. Med., 34: 252-260. https://doi.org/10.1002/(SICI)1097-0274(199809)34:3\u0026lt;252::AID-AJIM7\u0026gt;3.0.CO;2-X\u003c/li\u003e\n\u003cli\u003ePiel C, Pouchieu C, Tual S, Migault L, Lemarchand C, Carles C, Boulanger M, Gruber A, Rondeau V, Marcotullio E, Lebailly P, Baldi I; AGRICAN group. Central nervous system tumors and agricultural exposures in the prospective cohort AGRICAN. Int J Cancer. 2017 Nov 1;141(9):1771-1782. doi: 10.1002/ijc.30879.\u003c/li\u003e\n\u003cli\u003eGatto NM, Ogata P, Lytle B. Farming, Pesticides, and Brain Cancer: A 20-Year Updated Systematic Literature Review and Meta-Analysis. Cancers (Basel). 2021 Sep 5;13(17):4477. doi: 10.3390/cancers13174477.\u003c/li\u003e\n\u003cli\u003eRuder AM, Waters MA, Butler MA, Carre\u0026oacute;n T, Calvert GM, Davis-King KE, Schulte PA, Sanderson WT, Ward EM, Connally LB, Heineman EF, Mandel JS, Morton RF, Reding DJ, Rosenman KD, Talaska G; Brain Cancer Collaborative Study Group. Gliomas and farm pesticide exposure in men: the upper midwest health study. Arch Environ Health. 2004 Dec;59(12):650-7. doi: 10.1080/00039890409602949.\u003c/li\u003e\n\u003cli\u003ePouchieu C., I. Baldi, A. Gruber, E. Berteaud, C. Carles, H. Loiseau. Descriptive epidemiology and risk factors of primary central nervous system tumors: Current knowledge. Revue Neurologique, Volume 172, Issue 1, 2016, Pages 46-55, https://doi.org/10.1016/j.neurol.2015.10.007. \u003c/li\u003e\n\u003cli\u003ePreston-Martin, S.; Lewis, S.; Winkelmann, R.; Borman, B.; Auld, J.; Pearce, N. Descriptive epidemiology of primary cancer of the brain, cranial nerves, and cranial meninges in New Zealand, 1948\u0026ndash;88. Cancer Causes Control 1993, 4, 529\u0026ndash;538.\u003c/li\u003e\n\u003cli\u003eSturm, E.T., Castro, C., Mendez-Colmenares, A. Duffy, J. , Burzynska, A.Z.; Stallones, L., Thomas, M.L. Risk Factors for Brain Health in Agricultural Work: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 3373. https://doi.org/10.3390/ijerph19063373\u003c/li\u003e\n\u003cli\u003eParr\u0026oacute;n T., Mar Requena, Antonio F. Hern\u0026aacute;ndez, Raquel Alarc\u0026oacute;n. Association between environmental exposure to pesticides and neurodegenerative diseases, Toxicology and Applied Pharmacology, Volume 256, Issue 3, 2011, 379-385, https://doi.org/10.1016/j.taap.2011.05.006.\u003c/li\u003e\n\u003cli\u003eNandipati S, Litvan I. Environmental Exposures and Parkinson\u0026apos;s Disease. Int J Environ Res Public Health. 2016 Sep 3;13(9):881. doi: 10.3390/ijerph13090881.\u003c/li\u003e\n\u003cli\u003eAlarc\u0026oacute;n R, Gim\u0026eacute;nez B, Hern\u0026aacute;ndez AF, L\u0026oacute;pez-Vill\u0026eacute;n A, Parr\u0026oacute;n T, Garc\u0026iacute;a-Gonz\u0026aacute;lez J, Requena M. Occupational exposure to pesticides as a potential risk factor for epilepsy. Neurotoxicology. 2023 May;96:166-173. doi: 10.1016/j.neuro.2023.04.012.\u003c/li\u003e\n\u003cli\u003eDardiotis E, Skouras P, Varvarelis OP, Aloizou AM, Hern\u0026aacute;ndez AF, Liampas I, Rikos D, Dastamani M, Golokhvast KS, Bogdanos DP, Tsatsakis A, Siokas V, Mitsias PD, Hadjigeorgiou GM. Pesticides and tremor: An overview of association, mechanisms and confounders. Environ Res. 2023 Jul 15;229:115442. doi: 10.1016/j.envres.2023.115442.\u003c/li\u003e\n\u003cli\u003eLucero B and Mu\u0026ntilde;oz-Quezada MT. Neurobehavioral, Neuromotor, and Neurocognitive Effects in Agricultural Workers and Their Children Exposed to Pyrethroid Pesticides: A Review. Front. Hum. Neurosci. 15:648171. doi: 10.3389/fnhum.2021.648171\u003c/li\u003e\n\u003cli\u003ePerrin L, Spinosi J, Chaperon L, Kab S, Moisan F, Ebaz A. Pesticides expenditures by farming type and incidence of Parkinson disease in farmers: A French nationwide study. Environ Res. 2021 Jun;197:111161. doi: 10.1016/j.envres.2021.111161.\u003c/li\u003e\n\u003cli\u003eGunnarsson LG, Bodin L. Parkinson\u0026apos;s disease and occupational exposures: a systematic literature review and meta-analyses. Scand J Work Environ Health. 2017 May 1;43(3):197-209. doi: 10.5271/sjweh.3641.\u003c/li\u003e\n\u003cli\u003eGunnarsson LG, Bodin L. Amyotrophic Lateral Sclerosis and Occupational Exposures: A Systematic Literature Review and Meta-Analyses. Int J Environ Res Public Health. 2018 Oct 26;15(11):2371. doi: 10.3390/ijerph15112371.\u003c/li\u003e\n\u003cli\u003eGunnarsson LG, Bodin L. Occupational Exposures and Neurodegenerative Diseases-A Systematic Literature Review and Meta-Analyses. Int J Environ Res Public Health. 2019 Jan 26;16(3):337. doi: 10.3390/ijerph16030337.\u003c/li\u003e\n\u003cli\u003eKyrozis, A.; Ghika, A.; Stathopoulos, P.; Vassilopoulos, D.; Trichopoulos, D.; Trichopoulou, A. Dietary and lifestyle variables in relation to incidence of Parkinson\u0026rsquo;s disease in Greece. Eur. J. Epidemiol. 2013, 28, 67\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eFirestone, J.A.; Lundin, J.I.; Powers, K.M.; Smith-Weller, T.; Franklin, G.M.; Swanson, P.D.; Longstreth, W.; Checkoway, H. Occupational factors and risk of Parkinson\u0026rsquo;s disease: A population-based case\u0026ndash;control study. Am. J. Ind. Med. 2009, 53, 217\u0026ndash;223.\u003c/li\u003e\n\u003cli\u003eRocca, W.A.; Anderson, D.W.; Meneghini, F.; Grigoletto, F.; Morgante, L.; Reggio, A.; Savettieri, G.; Di Perri, R. Occupation, education, and Parkinson\u0026rsquo;s disease: A case-control study in an Italian population. Mov. Disord. 1996, 11, 201\u0026ndash;206.\u003c/li\u003e\n\u003cli\u003eKab, S.; Moisan, F.; Elbaz, A. Farming and incidence of motor neuron disease: French nationwide study. Eur. J. Neurol. 2017, 24, 1191\u0026ndash;1195.\u003c/li\u003e\n\u003cli\u003eMassari S., Malpassuti V.C., Binazzi A., Paris L., Gariazzo C., Marinaccio A. Occupational Mortality Matrix: A Tool for Epidemiological Assessment of Work-Related Risk Based on Current Data Sources. Int. J. Environ. Res. Public Health 2022, 19, 5652.\u003c/li\u003e\n\u003cli\u003eEurostat. NACE Rev. 2\u0026mdash;Statistical Classification of Economic Activities in the European Community; Eurostat: Luxembourg, 2008; ISBN 978-92-79-04741-1.\u003c/li\u003e\n\u003cli\u003eAlavanja MC, Bonner MR. Occupational pesticide exposures and cancer risk: a review. J Toxicol Environ Health B Crit Rev. 2012;15(4):238-63. doi: 10.1080/10937404.2012.632358. PMID: 22571220; PMCID: PMC6276799.\u003c/li\u003e\n\u003cli\u003eAlavanja, M.C.R., Ross, M.K. and Bonner, M.R. (2013), Increased cancer burden among pesticide applicators and others due to pesticide exposure. CA: A Cancer Journal for Clinicians, 63: 120-142. https://doi.org/10.3322/caac.21170\u003c/li\u003e\n\u003cli\u003eSkeie, G.; Muller, B.; Haugarvoll, K.; Larsen, J.; Tysnes, O. Differential effect of environmental risk factors on postural instability gait difficulties and tremor dominant Parkinson\u0026rsquo;s disease. Mov. Disord. 2010, 25, 1847\u0026ndash;1852.\u003c/li\u003e\n\u003cli\u003eKirkey, K.L.; Johnson, C.C.; Rybicki, B.A.; Peterson, E.L.; Kortsha, G.X.; Gorell, J.M. Occupational categories at risk for Parkinson\u0026rsquo;s disease. Am. J. Ind. Med. 2001, 39, 564\u0026ndash;571.\u003c/li\u003e\n\u003cli\u003eZorzon, M.; Capus, L.; Pellegrino, A.; Cazzato, G.; Zivadinov, R. Familial and environmental risk factors in Parkinson\u0026rsquo;s disease: A case-control study in north-east Italy. Acta Neurol. Scand. 2002, 105, 77\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eGlass, T.; Dalvie, M.A.; Holtman, Z.; Vorster, A.A.; Ramesar, R.S.; London, L. DNA variants and organophosphate neurotoxicity among emerging farmers in the Western Cape of South Africa. Am. J. Ind. Med. 2017, 61, 11\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eDutheil, F.; Beaune, P.; Tzourio, C.; Loriot, M.-A.; Elbaz, A. Interaction between ABCB1 and Professional Exposure to Organochlorine Insecticides in Parkinson Disease. Arch. Neurol. 2010, 67, 739\u0026ndash;745.\u003c/li\u003e\n\u003cli\u003eRitz, B.R.; Manthripragada, A.D.; Costello, S.; Lincoln, S.J.; Farrer, M.J.; Cockburn, M.; Bronstein, J. Dopamine Transporter Genetic Variants and Pesticides in Parkinson\u0026rsquo;s Disease. Environ. Health Perspect. 2009, 117, 964\u0026ndash;969.\u003c/li\u003e\n\u003cli\u003eGangemi S, Miozzi E, Teodoro M, Briguglio G, De Luca A, Alibrando C, Polito I, Libra M. Occupational exposure to pesticides as a possible risk factor for the development of chronic diseases in humans (Review). Mol Med Rep. 2016 Nov;14(5):4475-4488. doi: 10.3892/mmr.2016.5817.\u003c/li\u003e\n\u003cli\u003eKamel F, Umbach DM, Bedlack RS, Richards M, Watson M, Alavanja MC, Blair A, Hoppin JA, Schmidt S and Sandler DP: Pesticide exposure and amyotrophic lateral sclerosis. Neurotoxicology 33: 457 462, 2012.\u003c/li\u003e\n\u003cli\u003eKang H, Cha ES, Choi GJ and Lee WJ: Amyotrophic lateral sclerosis and agricultural environments: A systematic review. J Korean Med Sci 29: 1610 1617, 2014.\u003c/li\u003e\n\u003cli\u003eFilippini T, Tesauro M, Fiore M, et al. Environmental and Occupational Risk Factors of Amyotrophic Lateral Sclerosis: A Population-Based Case-Control Study. Int J Environ Res Public Health. 2020 Apr 22;17(8):2882. doi: 10.3390/ijerph17082882.\u003c/li\u003e\n\u003cli\u003eInternational Agency for Research on Cancer. DDT, Lindane, and 2,4-D; IARC Monographs on the Evaluation of Carcinogenic Risks to Humans Volume 113; IARC, World Health Organization: Lyon, France, 2016, ISBN 978-92-832-0179-3.\u003c/li\u003e\n\u003cli\u003eCogliano, V.J.; Baan, R.; Straif, K.; Grosse, Y.; Lauby-Secretan, B.; El Ghissassi, F.; Bouvard, V.; Benbrahim-Tallaa, L.; Guha, N.; Freeman, C.; et al. Preventable exposures associated with human cancers. J. Natl. Cancer Inst. 2011, 103, 1827\u0026ndash;1839.\u003c/li\u003e\n\u003cli\u003eFallahi P, Foddis R, Cristaudo A, Antonelli A. High risk of brain tumors in farmers: a mini-review of the literature, and report of the results of a case control study. Clin Ter. 2017 Sep-Oct;168(5):e290-e292. doi: 10.7417/T.2017.2022.\u003c/li\u003e\n\u003cli\u003eFaustini A, F Forastiere, L Di Betta, E M Magliola, C A Perucci. Cohort study of mortality among farmers and agricultural workers. Med Lav. 1993 Jan-Feb;84(1):31-41.\u003c/li\u003e\n\u003cli\u003eTorchio P, Lepore AR, Corrao G, Comba P, Settimi L, Belli S, Magnani C, di Orio F. Mortality study on a cohort of Italian licensed pesticide users. Sci Total Environ. 1994 Jun 20;149(3):183-91. doi: 10.1016/0048-9697(94)90178-3.\u003c/li\u003e\n\u003cli\u003eRonco G, Costa G, Lynge E. Cancer risk among Danish and Italian farmers. Br J Ind Med. 1992 Apr;49(4):220-5. doi: 10.1136/oem.49.4.220.\u003c/li\u003e\n\u003cli\u003eForastiere F, Quercia A, Miceli M, Settimi L, Terenzoni B, Rapiti E, Faustini A, Borgia P, Cavariani F, Perucci CA. Cancer among farmers in central Italy. Scand J Work Environ Health. 1993 Dec;19(6):382-9. doi: 10.5271/sjweh.1458.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eNumber of deaths. Blue-collar, low educated workers, Italy 2005-2018.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"499\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.48496993987976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSector of employment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.857715430861724%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelection criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.65731462925852%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of deaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.48496993987976%\" valign=\"top\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.857715430861724%\" valign=\"top\"\u003e\n \u003cp\u003eRegistered in the INPS archive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.65731462925852%\" valign=\"top\"\u003e\n \u003cp\u003e1,634,187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.48496993987976%\" valign=\"top\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.857715430861724%\" valign=\"top\"\u003e\n \u003cp\u003eRegistered in the INPS archive with permanence and prevalence of a working sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.65731462925852%\" valign=\"top\"\u003e\n \u003cp\u003e1,210,709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.48496993987976%\" valign=\"top\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.857715430861724%\" valign=\"top\"\u003e\n \u003cp\u003eAs above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.65731462925852%\" valign=\"top\"\u003e\n \u003cp\u003e64,200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.48496993987976%\" valign=\"top\"\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.857715430861724%\" valign=\"top\"\u003e\n \u003cp\u003eAs above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.65731462925852%\" valign=\"top\"\u003e\n \u003cp\u003e107,650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u003c/strong\u003eCharacteristics of study population included in the study, Italy, 2005-2018.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"430\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgriculture (cases)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eServices (controls)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.972027972027973%\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.445221445221446%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.407925407925408%\" valign=\"top\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\" style=\"width: 17.0079%;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e48,058 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e55,871 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e16,142 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e51,779 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" rowspan=\"3\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eAge of death (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e1st Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e3rd Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" rowspan=\"2\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eMiddle school diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e9,599 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e24,122 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003ePrimary school diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e54,601 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e83,528 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" rowspan=\"3\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eLength of employment (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e1st Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e3rd Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" rowspan=\"3\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eYear of end employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e1st Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e1981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e1986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e3rd Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\" rowspan=\"3\" valign=\"top\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eAge at first employment (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3953488372093%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e1st Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.348837209302324%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.790697674418606%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.677419354838708%\" valign=\"bottom\" style=\"width: 26.2205%;\"\u003e\n \u003cp\u003e3rd Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.16129032258065%\" valign=\"bottom\" style=\"width: 31.8898%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.612903225806452%\" valign=\"bottom\" style=\"width: 15.5906%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eNumber of individuals.\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMortality odds ratios (MOR) and 95% confidence intervals (CI) of mental, behavioral and neurodevelopmental disorders and nervous system diseases** in the agricultural sector (main analysis and by gender), Italy, 2005\u0026ndash;2018. MORs are calculated in reference to the service sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of death (ICD-10 codes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental, Behavioral and Neurodevelopmental disorders (F01-F99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,126 (63,599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e846 (47,212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e280 (15,862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular dementia (F01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294 (63,906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230 (47,828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64 (16,078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified dementia (F03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e716 (63,484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e528 (47,530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e188 (15,954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases of the nervous system (G00-G99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2513 (61,687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1805 (46,253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e708 (15,434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinal muscular atrophy (SMA) (G12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261 (63,939)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160 (47,898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e101 (16,041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParkinson's disease (G20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e742 (63458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e620 (47,438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e122 (16,020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlzheimer's disease (G30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e865 (63,335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e568 (47,490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e297 (15,845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther degenerative diseases of nervous system and disorders of brain (G31; G93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208 (63,992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157 (47,901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51 (16,091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple sclerosis (G35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (64,181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (48,051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12 (16,130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpilepsy (G40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (64,124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (48,002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20 (16,122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Adjusted for or stratified by gender, age class, educational level, year of death, and region of residence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e** The reported causes of death are those that scientific literature has indicated as possible associations with occupational risks in agriculture.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMortality odds ratios (MOR) and 95% confidence intervals (CI) of malignant neoplasms** of lymphoid, hematopoietic and related tissue in the agricultural sector (main analysis and by gender), Italy, 2005\u0026ndash;2018. MORs are calculated in reference to the service sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of death (ICD-10 codes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasms of lymphoid, hematopoietic and related tissue (C81-C96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,222 (61,978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,566 (46,492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e656 (15,486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHodgkin lymphoma (C81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (64,153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (48,030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19 (16,123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-follicular lymphoma (C83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (64,118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60 (47,998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22 (16,120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther specified and unspecified types of non-Hodgkin lymphoma (C85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e567 (63,682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e405 (47,653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e162 (15,980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant immunoproliferative diseases (C88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (64,170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (48,038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10 (16,132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e7.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e30.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple myeloma (C90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e546 (63,654)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e366 (47,692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e180 (15,962)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoid leukemia (C91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (63,928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (47,854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68 (16,074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyeloid leukemia (C92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474 (63,726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e330 (47,728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e144 (15,998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukemia of unspecified cell (C95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (64,058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (47,957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41 (16,101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Adjusted for or stratified by gender, age class, educational level, year of death, and region of residence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e** The reported causes of death are those that scientific literature has indicated as possible associations with occupational risks in agriculture.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMortality odds ratios (MOR) and 95% confidence intervals (CI) of other cancer diseases** in the agricultural sector (main analysis and by gender), Italy, 2005\u0026ndash;2018. MORs are calculated in reference to the service sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of death (ICD-10 codes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eCases (controls)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eAdjusted MOR (95%CI)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of Lip (C00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (64,184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (48,044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2 (16,140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of pharynx (C09-C10, C12-C14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154 (64,046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 (47,918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14 (16,128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of nasopharynx (C11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (64,184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (48,044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2 (16,140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of stomach (C16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,732 (62,468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,291 (46,767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e441 (15,701)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of colon-rectum (C18-C21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,815 (61,385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,068 (45,990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e747 (15,395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of liver and intrahepatic bile ducts (C22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,259 (62,941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e994 (47,064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e265 (15,877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of sinuses and nasal cavity (C30.0, C31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (64,175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (48,041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8 (16,134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of larynx (C32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (63,983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198 (47,860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19 (16,123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of trachea, bronchus and lung (C33, C34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,201 (59,999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,623 (44,435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e578 (15,564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma of skin (C43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (63,920)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e188 (47,870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92 (16,050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of connective and soft tissue (C49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (64,090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (47,983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 (16,107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of breast (C50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,014 (63,186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (48,033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e989 (15,153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of ovary (C56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421 (63,779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e421(63,779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of prostate (C61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,582 (62,618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1582 (62,618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of kidney (C64-C66, C68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e724 (63,476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e575 (47,483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e149 (15,993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of bladder (C67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e672 (63,528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e603 (47,455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69 (16,073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasm of brain and central nervous system (C71, C72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601 (63,599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e376 (47,682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e225 (15,917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Adjusted for or stratified by gender, age class, educational level, year of death, and region of residence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNC, not calculated\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e** The reported causes of death are those that scientific literature has indicated as possible associations with occupational risks in agriculture.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s, spinal muscular atrophy, leukemia, lymphoma, brain, central nervous system","lastPublishedDoi":"10.21203/rs.3.rs-4268499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4268499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePopulation working or living in agriculture settings may experience important exposure to pesticides and other agents. Some health effects associated with them are well known (e.g. skin cancer due to solar radiation) while for others (e.g., neurological diseases and lymphoid, hematopoietic and related tissue cancers) additional epidemiological evidence is needed. We aim to investigate mortality for neurological diseases and cancer in workers employed in agriculture in Italy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed a case-control study based on countrywide Italian mortality data 2005\u0026ndash;2018 linked with National Social Insurance data to retrieve information on working histories. Adjusted cancer specific mortality odds ratios (MOR) were calculated. We modelled occupational exposure as \u0026ldquo;ever/never been employed\u0026rdquo; in agriculture, using the service sectors as reference. Analysis was stratified for gender and length of employment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAbout 64,000 workers employed in agriculture were analyzed in comparison with a control group of 107,000 workers of the service sector. We found elevated risk in agriculture workers for mortality from spinal muscular atrophy (MOR 1.26, 95% CI: 1.03\u0026ndash;1.56; 261 deaths) and Parkinson\u0026rsquo;s disease (PD) (MOR 1.16, 95% CI:1.00-1.34; 742 deaths). As for cancer mortality, positive associations were found for non-follicular lymphoma (NFL) (MOR 1.59, 95% CI: 1.03\u0026ndash;2.46; 82 deaths), multiple myeloma (MM) (MOR 1.42, 95% CI: 1.22\u0026ndash;1.65; 546 deaths) and myeloid leukemia (ML) (MOR 1.36, 95% CI:1.16\u0026ndash;1.60; 474 deaths), as well as for stomach (MOR 1.30, 95% CI:1.20\u0026ndash;1.41; 1,732 deaths), prostate (MOR 2.03, 95% CI:1.85\u0026ndash;2.24, 1,582 deaths), and brain and central nervous system cancer (MOR 1.30, 95% CI:1.13\u0026ndash;1.50, 601 deaths). PD, NFL and ML, as well as cancers of skin, connective and soft tissue, prostate and brain were found to involve mainly men.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLong-term employment in agriculture was demonstrated associated with several health risks, some of which could be explained by exposure to pesticides. Although the use of the different agronomic categories of pesticides has been changed over time and some active ingredients were prohibited or limited, their health effects remain of concern for their large use, demanding for further focused investigations and preventive measures.\u003c/p\u003e","manuscriptTitle":"Mortality from nervous system diseases and cancer in agriculture workers: a case-control study in Italy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 16:21:33","doi":"10.21203/rs.3.rs-4268499/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-22T10:11:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T05:50:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T05:50:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-04-15T09:00:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a24dd59-947f-415e-87b9-8b55aaf7a9ee","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T11:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 16:21:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4268499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4268499","identity":"rs-4268499","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}