Contribution of Long-Term Exposure to Outdoor Black Carbon to the Carcinogenicity of Air Pollution: Evidence regarding Risk of Cancer in the Gazel Cohort
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 3 Linguagem: Inglês
10.1289/ehp8719
ISSN1552-9924
AutoresÉmeline Lequy, Jack Siemiatycki, Kees de Hoogh, Danielle Vienneau, Jean‐François Dupuy, Valérie Garès, Ole Hertel, Jesper H. Christensen, Sergey Zhivin, Marcel Goldberg, Marie Zins, Bénédicte Jacquemin,
Tópico(s)Air Quality Monitoring and Forecasting
ResumoVol. 129, No. 3 ResearchOpen AccessContribution of Long-Term Exposure to Outdoor Black Carbon to the Carcinogenicity of Air Pollution: Evidence regarding Risk of Cancer in the Gazel Cohort Emeline Lequy, Jack Siemiatycki, Kees de Hoogh, Danielle Vienneau, Jean-François Dupuy, Valérie Garès, Ole Hertel, Jesper Heile Christensen, Sergey Zhivin, Marcel Goldberg, Marie Zins, and Bénédicte Jacquemin Emeline Lequy Address correspondence to Emeline Lequy, UMS 011, Hôpital Paul Brousse, 16 avenue Paul Vaillant Couturier, 94807 VILLEJUIF CEDEX, France. Telephone number: +33 (0)1 77 74 74 19. Email: E-mail Address: [email protected] and Bénédicte Jacquemin, Irset, 9 avenue du Prof. Léon Bernard, 35000 RENNES, France. Telephone number: +33 (0)2 23 23 7769. Email: E-mail Address: [email protected] UMS 011, Institut national de la santé et de la recherché médicale (Inserm), Villejuif, France Centre de recherche du centre hospitalier de l'université de Montréal, Université de Montréal, Québec, Canada , Jack Siemiatycki Centre de recherche du centre hospitalier de l'université de Montréal, Université de Montréal, Québec, Canada , Kees de Hoogh Swiss Tropical and Public Health Institute, Basel, Switzerland University of Basel, Basel, Switzerland , Danielle Vienneau Swiss Tropical and Public Health Institute, Basel, Switzerland University of Basel, Basel, Switzerland , Jean-François Dupuy UMR 6625 IRMAR, INSA, CNRS, Université de Rennes, Rennes, France , Valérie Garès UMR 6625 IRMAR, INSA, CNRS, Université de Rennes, Rennes, France , Ole Hertel Department of Environmental Science, Aarhus University, Roskilde, Denmark , Jesper Heile Christensen Department of Environmental Science, Aarhus University, Roskilde, Denmark , Sergey Zhivin UMS 011, Institut national de la santé et de la recherché médicale (Inserm), Villejuif, France , Marcel Goldberg UMS 011, Institut national de la santé et de la recherché médicale (Inserm), Villejuif, France , Marie Zins UMS 011, Institut national de la santé et de la recherché médicale (Inserm), Villejuif, France , and Bénédicte Jacquemin Address correspondence to Emeline Lequy, UMS 011, Hôpital Paul Brousse, 16 avenue Paul Vaillant Couturier, 94807 VILLEJUIF CEDEX, France. Telephone number: +33 (0)1 77 74 74 19. Email: E-mail Address: [email protected] and Bénédicte Jacquemin, Irset, 9 avenue du Prof. Léon Bernard, 35000 RENNES, France. Telephone number: +33 (0)2 23 23 7769. Email: E-mail Address: [email protected] Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) – UMR_S 1085, Rennes, France Published:24 March 2021CID: 037005https://doi.org/10.1289/EHP8719AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Black carbon (BC), a component of fine particulate matter [particles with an aerodynamic diameter ≤2.5 μm (PM2.5)], may contribute to carcinogenic effects of air pollution. Until recently however, there has been little evidence to evaluate this hypothesis.Objective:This study aimed to estimate the associations between long-term exposure to BC and risk of cancer. This study was conducted within the French Gazel cohort of 20,625 subjects.Methods:We assessed exposure to BC by linking subjects' histories of residential addresses to a map of European black carbon levels in 2010 with back- and forward-extrapolation between 1989 and 2015. We used extended Cox models, with attained age as time-scale and time-varying cumulative exposure to BC, adjusted for relevant sociodemographic and lifestyle variables. To consider latency between exposure and cancer diagnosis, we implemented a 10-y lag, and as a sensitivity analysis, a lag of 2 y. To isolate the effect of BC from that of total PM2.5, we regressed BC on PM2.5 and used the residuals as the exposure variable.Results:During the 26-y follow-up period, there were 3,711 incident cancer cases (all sites combined) and 349 incident lung cancers. Median baseline exposure in 1989 was 2.65 10−5/m [interquartile range (IQR): 2.23–3.33], which generally slightly decreased over time. Using 10 y as a lag-time in our models, the adjusted hazard ratio per each IQR increase of the natural log-transformed cumulative BC was 1.17 (95% confidence interval: 1.06, 1.29) for all-sites cancer combined and 1.31 (0.93, 1.83) for lung cancer. Associations with BC residuals were also positive for both outcomes. Using 2 y as a lag-time, the results were similar.Discussion:Our findings for a cohort of French adults suggest that BC may partly explain the association between PM2.5 and lung cancer. Additional studies are needed to confirm our results and further disentangle the effects of BC, total PM2.5, and other constituents. https://doi.org/10.1289/EHP8719IntroductionStrong evidence over recent decades allowed classifying outdoor air pollution and fine particulate matter [fine particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5)] as carcinogenic (Loomis et al. 2013; Pedersen et al. 2017; Raaschou-Nielsen et al. 2013; IARC 2016). Yet the separate effects of each PM2.5 component (sulfates, nitrate, ammonium, organics, metals, etc.) are rarely quantified (Beelen et al. 2015; Ostro et al. 2011; Raaschou-Nielsen et al. 2016). Black carbon (BC), a component of PM2.5, comes from incomplete combustion processes, mainly from anthropogenic sources such as fossil fuel or biomass burning (Chylek et al. 2015; Koelmans et al. 2006). The first health concerns about exposure to BC appeared decades ago (Mumford et al. 1990); since then, reports have accumulated linking exposure to BC with increased morbidity and mortality, including lung cancer mortality (Anenberg et al. 2012; Grahame et al. 2014; Hvidtfeldt et al. 2019; Yang et al. 2019), lower lung function and slower cognitive development in children (Paunescu et al. 2019; Sunyer et al. 2015), increased bone loss (Prada et al. 2017), and decreased cognitive functions in the elderly (Colicino et al. 2017; Wurth et al. 2018). Although evidence has accumulated on toxicity of BC, we still know little about the effects of chronic low-level exposure on cancer risk, partly because the paucity of available data on general population long-term exposure to BC left little opportunity for such studies. Recently, the ELAPSE project estimated annual outdoor BC concentrations between 1990 and 2015 at fine resolution over Europe (de Hoogh et al. 2018). In this study, we aimed to investigate the relationships between long-term exposure to BC and incident all-site and lung cancer in the population-based French cohort Gazel with a 26-y follow-up.Material and MethodsStudy PopulationThe Gazel cohort enrolled 20,625 participants in 1989 from the French national gas and energy company, Electricité-de-France Gaz-de-France (Goldberg et al. 2015). These participants, aged 35–50 y at enrollment, completed a baseline detailed self-administered questionnaire, then a follow-up questionnaire sent every year – with a high response rate during the follow-up (>80% from 1990 to 1992, and >70% from 1993 to 2015). Participants' histories of main residential addresses were collected and geocoded for each year since 1989. To minimize misclassification while retaining the largest possible number of participants, we excluded participants with the poorest exposure coverage [i.e., more than 20% of missing geocodes during their follow-up (due to stays abroad mainland France)]. This residential history was collected on an annual basis, so each address corresponds to a calendar year. We used last-observation carried forward to impute any missing addresses for the concerned participants. We identified the numbers of residential changes during the study period by choosing a threshold of 1-km difference in the geocodes to identify a substantial residential change; during the study period, we observed 13,981 of changes of more than 1 km, for 9,112 participants. Geocoding precision ranged from postal code (13%) to address level (48%).We excluded 823 participants with any primary incident cancer diagnosed or censored before 1999, to take account of a potential 10-year lag between exposure and incidence/censoring (see "Statistical Analyses" section). This approach led to a slightly different study population for analyses on all-site and lung cancer. We also excluded 90 participants who were lost on follow-up (because they definitively left the company) or who asked to be removed from the study and their data to be deleted. Our study included participants who died during the follow-up without a diagnosis of cancer and who were censored at the date of death. Further, in the lung cancer analysis, we compared lung cancer cases to subjects not developing any cancer; thus we excluded participants with other cancers at any time during the study period (1999–2015) from the study population. Our study population included 19,348 and 15,694 participants for the analyses on primary incident all-site and primary incident lung cancer (Figure S1).The Gazel study protocol was approved by the French authority for data confidentiality (Commission Nationale de l'Informatique et des Libertés No. 105,728) and by the Ethics Evaluation Committee of the Institut national de la santé et de la recherche médicale (Inserm, National Institute of Health and Medical Research) (IRB0000388, FWA00005831). The invitation to participate was sent by post to eligible persons, accompanied by a document detailing the project, the voluntary nature of their participation, the data collected, the conditions of security and confidentiality and the future use of the data. The subjects solicited were invited to complete a questionnaire indicating their consent.Cancer IncidenceIncident cancer cases were ascertained from three sources: a) French national health administrative databases containing listings of incident cancers (more details below) during the study period (1999–2015); b) company records which have systematically recorded all cancers (except nonmelanoma skin cancers) diagnosed among their current employees [with pathology reports and the date of diagnosis, and coded according to the International Classification of Diseases (ICD)] (Goldberg et al. 1996); and c) cancer diagnoses self-reported by participants via the follow-up questionnaires from 2008 onward. Participants who gave consent were contacted for collecting medical information to obtain the date of diagnosis and the type of cancer. The linkage of participants to the French national health administrative databases that record each use of the health system allowed identifying cancer from data on hospitalizations and from the "Chronic Diseases" register (diseases including cancer for which all the treatment is reimbursed) with dates and diagnoses (Tuppin et al. 2017).The ICD-10 classification system was used to code the type of cancer, with the whole ICD-10 chapter except C77–79 (secondary malignant neoplasms) and C44 (nonmelanoma skin cancers); we used C34 to identify lung cancer.Exposure AssessmentFor each subject of our study population, we estimated BC, PM2.5, and nitrogen dioxide (NO2) exposure in each year from 1989 to 2005, based on the subject's residential address linked to data from land use regression (LUR) models developed at a fine spatial scale (100×100 m) over Europe (de Hoogh et al. 2018). This linkage also accounts for any residential address change over the years (see "Study Population" section). PM2.5 and BC measurement data came from two sources: PM2.5 absorbance in samples collected in the ESCAPE project for BC (436 sites), and from regulatory monitoring data maintained in the AirBase European database for PM2.5 (543 sites). For the year 2010, LUR models were developed by regressing the measured pollutant concentrations against a range of predictor variables (including land-use variables, road density, and altitude, as well as satellite-derived and chemical transport modeled pollutant estimates) followed for PM2.5 only by universal kriging to explain spatial autocorrelation in the residuals. The full model (based on all monitoring sites) explained 72%, 54%, and 59% of PM2.5, BC, and NO2 respectively. Models were further validated, and shown to be robust, using a five hold out validation strategy which explained 66, 51, and 57% of the spatial variation in the respective measured PM2.5, BC, and NO2 concentrations (de Hoogh et al. 2018). Finally, the estimated concentrations for 2010 were rescaled annually for the years 1990–2015, by European Nomenclature of Territorial Units for Statistics -1 (NUTS-1) regions (i.e., European Union–defined administrative regions within countries) in France, using back- and forward extrapolation. This rescaling process was based on annual mean estimates (1990–2015) from the 26×26 km Danish Eulerian Hemispheric Model, downscaled from the original 50×50 km resolution using bilinear interpolation (Brandt et al. 2012). In addition, in this study, we further back-extrapolated PM2.5 exposure to 1989. To visualize spatiotemporal differences in BC exposure over France, we mapped the differences in BC exposure over France for Gazel nonmover participants between periods 1995–2000 and 2000–2005. We calculated the relative change in %, for each nonmover participant for each pair of years. To improve the maps' readability, we averaged the results on a 5×5 km2 grid.CovariablesBased on recognized and suspected risk factors, we a priori selected the following sets of variables as potential confounders and/or effect modifiers:Sociodemographic and occupational variables.Sex, education (attending school for 6–11 y, 12–13 y, 14–15 y, other secondary education, other diploma), and socioeconomic status (SES; low: blue-collar workers or clerks; medium: first-line supervisors or sales representatives; high: management), all at baseline. We also included a synthetic summary of occupational exposure to nine known lung carcinogens (asbestos, cadmium, chlorinated solvents, chromium, coal gasification, coal-tar pitch, creosotes, crystalline silica, and hydrazine) over the whole employment period [categorized into none, one, two, or at least three carcinogens, from each Gazel participant's career-long history linked to the French job-exposure matrix MATEX (Imbernon 1991)].Lifestyle variables.Time-varying variables for tobacco (cumulative smoking pack-years), alcohol intake (abstinent, light drinker, moderate drinker, heavy drinker, unclear pattern), family status (single or not), body mass index (BMI, weight in kilograms divided by the square of height in meters). Some questions were only asked occasionally, such as passive smoking at home or at work (yes or no) in 1990 and 1996, or fruit and vegetable intake (never or less than once a week; once or twice a week; more than twice a week but not every day; every day or almost) in 1998, 2004, 2009 and 2014. We processed these two variables to make them time-varying annually, attributing the data collected in 1990 and 1996 to each year between 1989–1995 and 1996–2015, respectively, for passive smoking, and the data collected in 1998, 2004, 2009, and 2014 to each year between 1989–1998, 1999–2004, 2005–2010, and 2011–2015, respectively, for fruit and vegetable intake.Contextual variables.For all participants and every year, we calculated the distance to the nearest major road. At the municipality level, we obtained the population density in 1989, 2000, and 2010, from which we derived a urban classification: urban (high population density), semiurban (intermediate pop density), and rural (low population density). The population density cut-offs are based on the European urban/rural classification. To define whether the participants had lived solely in urban, semiurban, rural area, or in mixed areas over all the follow-up, we used the information from the 3 y for which we obtained such data. Also at the municipality level, we obtained the French deprivation index (Rey et al. 2009) calculated for 2009 for all participants who were still alive and therefore geocoded in 2009. To take into account participants who died before this variable could be calculated in 2009, we categorized the values from the French deprivation index into tertiles and added the missing values as a category so as to not lose any participant in the analyses because of this variable.ImputationsFor baseline variables, missing values ranged from 0% to 2.1% for sex and education, respectively. Throughout the follow-up, missing data ranged from 21% to 29% for alcohol consumption and BMI variables, respectively. We imputed all variables (except air pollution exposure and contextual variables) longitudinally for each participant using multiple imputations by chained equations from the R packages MICE and MICEADDS (van Buuren and Groothuis-Oudshoorn 2011), iterating 10 datasets 10 times with good convergence. All the variables described above were used as predictors. For the geographic variables (exposure to pollutants, deprivation index, distance to the road or urban classification), because the other predictors cannot predict them accurately, we recreated the initial missing values in our final dataset. We used the functions "2l.pmm" and "2l.only.pmm" for time-varying and time-independent variables, respectively. Following the MICE package manual ( https://cran.r-project.org/web/packages/mice/mice.pdf), we assessed convergence visually using Figure S2; the streams are supposed to mingle well and without showing a clear trend. Only for fruit and vegetable intake did we observe a poor mingling of the streams, due to the correlation between the responses to this question asked in 1999, 2004, 2009 and 2014. Because we aimed at pooling these values into one time-dependent categorical variable, we discarded this issue. Model-based estimates were pooled following Rubin's rules.Statistical AnalysesWe used extended Cox models with BC as a time-dependent variable, with attained age as underlying time scale, to validate the proportional hazard assumption, while annually describing the exposure to air pollutants. We estimated the associations between incident cancer and BC, as a single pollutant, or taking PM2.5 into account, following an approach based on residuals (Mostofsky et al. 2012). Since we have a rare opportunity to utilize a long time-series of BC exposure to study the long-term association with cancer incidence, we used cumulative exposures for each participant from baseline to incidence or censoring and adjusted for calendar time and age at inclusion (Pencina et al. 2007). 1. Single-pollutant approach: We included cumulative time-dependent black carbon exposure or cumulative time-dependent PM2.5 exposure or cumulative time-dependent NO2 exposure separately as a single pollutant in our main model. For these three pollutants, we used a spline function with 3 degrees of freedom (df) to test for nonlinearity. Based on visual inspection, the response to exposures approximately followed a natural logarithm-shaped curve for all-site cancer incidence (Figure S3), but not for cumulative NO2 and lung cancer. To consider these nonlinear relationships, and to facilitate interpreting the results of the Cox models, we natural log-transformed the cumulative annual BC and PM2.5 time-dependent exposures for both outcomes, and NO2 time-dependent exposure for all-site cancer only.2. Residual approach for black carbon: The Spearman's correlation coefficient between PM2.5 and BC was 0.74. Including both of those exposure variables in a regression model can distort the true effects of one or both of those variables. As an attempt to isolate the effect of BC from that of PM2.5, we followed the approach of Mostofsky et al. (2012), who suggested using the residuals of a regression between the constituent of PM2.5 and PM2.5 total mass. To do so, we first regressed BC (dependent variable) on PM2.5 (independent variable). The residuals of this regression should represent the variations of BC independently of PM2.5. When correctly specified, the residuals should be uncorrelated with PM2.5. We included BC as natural log-transformed cumulative exposures in the regression, and cumulative PM2.5 using a spline function with 4 df. This specification decorrelated the BC residuals from the cumulative PM2.5 exposure. In Cox models using BC residuals as exposure, the coefficient represents the risk associated with higher levels of cumulative black carbon exposure, while holding cumulative PM2.5 exposure constant. We did not further adjust for PM2.5, because it would provide information to interpret the effect of the other constituents of PM2.5, which was not the aim of this study. To test the independence of the effects of BC from those of NO2, we did the same analysis using the residuals of a regression between BC and NO2 because BC and NO2 were also highly correlated (Spearman's correlation coefficient of 0.89) and precluded a bi-pollutant analysis with the two pollutants in the same model.We provided hazard ratios for one interquartile range (IQR) of all variables of exposure to air pollution (after natural log-transforming cumulative BC and PM2.5 for both outcomes, as well as for cumulative NO2 for all-site cancer), and of the residual variables.To consider cancer latency, it is customary, especially when using time-varying variables in the statistical analyses, to discount exposures that occurred recently, because these are unlikely to have affected the cancer risk (Rothman et al. 2008). Thus, we implemented a 10-y lag period during which exposure was not counted for any time-dependent variables except for passive smoking and for fruit and vegetable consumption, which already included a time-lag due to the way these variables were collected and interpolated (therefore using passive smoking values in 1996 only). The inclusion of contextual variables was explored only in sensitivity analyses. We used several levels of adjustment when using BC as exposure: a) Model 1 included sex, and calendar time and age at inclusion as continuous variables; b) Model 2 also included cumulative smoking pack-years and passive smoking (yes/no as defined in 1996); and c) Model 3 (main model) was additionally adjusted for baseline education, SES, and occupational exposure to lung carcinogens and for time-varying alcohol consumption, family status, BMI, and fruit and vegetable consumption. We used the main model to derive estimates of the associations between PM2.5, NO2, or BC residuals and incident cancer. We calculated the Akaike Information Criterion (AIC) of each model, according to the level of adjustment or according to the exposure included.We used multiple imputations by chained equations to conduct all the analyses (see section below) unless specified otherwise.Sensitivity analyses were conducted for BC or PM2.5 separately (single-pollutant models) by: a) implementing a 2-y lag period with the same study population as that of the analysis implementing the 10-y lag period; and, after again implementing a 10-y lag period; by b) further adjusting for the French deprivation index; c) restricting the study population to the participants with address-level geocodes throughout their follow-up; d) restricting the study population to the participants with at least 20 y of follow-up (thus an exposure window of at least 10 y); and e) considering three other ways to deal with missing data: conducting these analyses with complete cases only, considering missing values as a category (therefore categorizing continuous variables by quartiles), or imputing missing data as the median (for continuous variables) or the mode (for categorical variables). As a further sensitivity analysis to explore the nonlinear relationship of the association between BC exposure and all-site cancer, we developed a two-piece linear model, by including an interaction term between BC exposure and a Boolean variable with the most appropriate cut-off found out to be at 24 10−5/m (as visually observed in Figure S3 and with the maximum likelihood among values from 15 to 30 10−5/m).For all-site cancer, to search for any effect modification by sex, smoking status, urban classification, and distance to the nearest major road, we used the single-pollutant models for BC or PM2.5 separately in single-pollutant models restricted to the following subsets: female or male, ever or never smokers, solely urban or solely semiurban or solely rural during the follow-up, and closer or farther than 500 m from the nearest major road over all the follow-up. Stratified analyses were not done for the analyses on lung cancer due to the small number of cases.We conducted all the analyses with R (version 3.5.1; R Development Core Team 2018) with the SURVIVAL package (Therneau 2015; Therneau and Grambsch 2000).ResultsStudy Population and ExposureStudy population characteristics are shown in Table 1. More than 70% were men. Mean age at baseline was 43.5, and median follow-up period was 27 y. Among those who eventually got cancer, the mean baseline age was 44.5 y, and the median follow-up until diagnosis was about 21 y. Most had 11 y or fewer of schooling, and most were employed in intermediate level jobs. Slightly fewer than half had been regular smokers at some time. Former and current smokers had cumulated 15.4 pack-years on average at baseline; those diagnosed with incident all-site or lung cancer cumulated 18 and 32 pack-years at baseline, respectively. We classified 71.4% participants as light drinkers, and 8.8% as heavy drinkers. Almost 70% of the participants ate fruit and vegetables almost every day and we calculated a median BMI of 24.3.Table 1 Baseline characteristics in 1989 (unless stated otherwise) of the study population for all 19,348 participants and according to their all-site cancer status over the period 1999–2015.Table 1, in three main columns, lists Categories, Individuals with a diagnosed cancer (all-site), and lowercase p. Individuals with a diagnosed cancer (all-site) column is sub divided into two columns, namely, No and Yes.Individuals with a diagnosed cancer (all-site):p-ValueNoYesNumber15,6373,711Follow-up time (y)27.0 [27.0, 27.0]20.3 [17.1, 23.7]<0.001Age (y)43.5 [41.0, 46.5]44.5 [42.0, 47.0]<0.001Sex<0.001 Male11,147 (71.3)2,956 (79.7) Female4,490 (28.7)755 (20.3)Smoking status<0.001 Never smoker6,859 (44.2)1,425 (38.8) Former smoker4,442 (28.6)1,075 (29.2) Current smoker4,210 (27.1)1,177 (32.0) Unknown12634Cumulative pack-yearsa15.0 [7.5, 25.7]17.7 [8.5, 29.6]<0.001 Unknown36192Passive smoking0.93 Yes10,623 (78.1)2,507 (78.0) No2,980 (21.9)707 (22.0) Unknown2,034497Education0.136 ≤11 y11,238 (73.5)2,720 (74.8) 12–13 y1,144 (7.5)227 (6.2) 14–15 y887 (5.8)207 (5.7) Other secondary education1,668 (10.9)398 (10.9) Other diploma357 (2.3)83 (2.3) Unknown34376Occupational exposureb<0.001 None9,443 (60.4)2,100 (56.6) One1,319 (8.4)328 (8.8) Two1,697 (10.9)448 (12.1) Three or more3,178 (20.3)835 (22.5)Alcohol use<0.001 Abstinent391 (2.5)104 (2.8) Light drinker11,170 (71.4)2,421 (65.3) Moderate drinker2,386 (15.3)654 (17.6) Heavy drinker1,370 (8.8)448 (12.1) Unclear pattern317 (2.0)83 (2.2) Unknown31Family status (not single)0.09 Not single13,722 (89.0)3,288 (90.0) Single1,696 (11.0)366 (10.0) Unknown21957Socioeconomic status0.011 Low (blue-collar workers or clerks)2,762 (17.7)621 (16.7) Intermediate (first-line supervisors)9,179 (58.8)2,130 (57.4) High (management)3,677 (23.5)958 (25.8) Unknown192Body mass indexc24.3 [22.3, 26.3]24.7 [22.8, 26.7]<0.001 Unknown2,253533Fruit and vegetable consumption0.123 Never or less than once a week81 (0.7)27 (1.0) Once or twice a week794 (7.0)211 (7.7) More than twice a week, not every day2,562 (22.6)641 (23.5) Every day or almost7,912 (69.7)1,851 (67.8) Unknown4,288981Distance to the nearest major road (km)d0.8 [0.3, 1.6]0.8 [0.3, 1.6]0.407Deprivation indexe0.983 High4,994 (33.3)1,211 (33.5) Intermediate4,986 (33.3)1,199 (33.1) Low5,007 (33.4)1,209 (33.4) Unknown65092Urban classificationf0.720 Solely urban3,911 (25.0)926 (25.0) Solely semiurban3,713 (23.7)878 (23.7) Solely rural2,921 (18.7)722 (19.5) Mixed5,092 (32.6)1,185 (31.9)Note: Number (percentage) or median (25th and 75th percentiles). All-site cancer cases were defined as the whole ICD-10 chapter except C77–79 (secondary malignant neoplasms) and C44 (nonmelanoma skin cancers). Participants were excluded from the analysis if they were diagnosed with cancer before 1999.aCalculated only for current and former smokers.bTo nine lung carcinogens over the whole employment period.cIn 1990.dUpdated annually. Median distance over the follow-up.eCalculated only for the residence in 2009 and for participants who were still alive then. Participants with "unknown" status were those who died before 2009.fUrban classification obtained only in 1989, 2000, and 2010.The exposure assessment yielded BC concentrations ranging between 0.7 and 8.9 10−5/m with a median of 1.9 between 1989 and 2015 (Table 2) with a modest decline over time (Figure 1) that differed slightly between regions (Supplementary Figure S4). The cumulative exposure ranged between 1.5 and 104.1 10−5/m with a median of 19.7. The exposure assessment yielded PM2.5 concentrations ranging from 2.6 to 57.3 μg/m3 with a median of 21.6 between 1989 and 2015, and a cumulative exposure ranging between 3.0 and 691.8 μg/m3 with a median of 252.8 (Figure 1; Table 2).Table 2 Exposure characteristics of the study population for all 19,348 participants of the Gazel cohort and according to their cancer status (all-site cancer) over the period 1999–2015.Table 2 has four main columns, namely, number of participants, all individuals, individuals with a diagnosed cancer (all-site), and lowercase italic p-Value. Individuals with a diagnosed cancer (all-site) column is subdivided into two columns, no and yes. All indivi
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