Key Features.
To investigate the potential carcinogenicity of tattooing that entails the intradermal injection of hazardous chemicals, the Cancer Risk Attributable to the Body Art of Tattooing (CRABAT) cohort was established within the French national cohort Constances.
The Constances cohort recruited >200 000 participants aged 18–69 years between 2012 and 2018 in the French metropolitan region. Participants in the Constances cohort are followed up annually and undergo a health examination every 5 years.
Basic tattoo exposure assessment was performed in 114 423 participants in 2020, who were subsequently enrolled in CRABAT.
Of the tattooed subset, detailed exposure was assessed in 2023 via complementary data collection y using the validated Epidemiological Tattoo Assessment Tool questionnaire that was answered by 7928 tattooed individuals (response rate 60%).
Tattoo exposure data are complemented by self-reported socio-demographic and lifestyle data from the Constances baseline and follow-up questionnaires. Outcome data are retrieved by individual record linkage to the French National Health database with a 2-year reporting delay, as well as self-reported and from quinqennial health examinations.
Tattoo exposure reassessment is planned for 2030.
Access to the data may be granted upon data-access application following the Constances terms of use and subsequent approval by the Institutional Steering Committee. For more information, please contact Milena Foerster (foersterm@iarc.who.int).
Why was the cohort set up?
Tattooing involves the injection of organic or inorganic pigment particles diluted in a carrier liquid into the dermis. Since the 1990s, tattoos have gained extreme popularity, with ≤40% in <40-year-olds in high-income countries being tattooed [1]. Still, few people are aware that, despite existing regulations in Europe, tattoo inks may contain hazardous chemicals, such as polycyclic aromatic hydrocarbons bound to carbon black particles, soluble primary aromatic amines in brightly coloured inks, and metallic impurities such as nickel and chromium, in all kinds of ink [2–4]. Many of these substances have been classified by the International Agency for Research on Cancer (IARC) as class 1, 2A, or 2B carcinogens, based on respiratory or oral exposure and/or topical application [5–7]. The potential health risks of the complex intradermal tattoo ink exposure leading to lymphatic accumulation of pigment particles remain under-researched [8, 9]. However, with more tattooed individuals, tattoo-related complications such as contact allergies or inflammatory skin conditions (e.g. granuloma or pseudo lymphoma) have become more frequent [9, 10]. Three published case–control studies investigated the skin tumour risk associated with tattooing, without showing a clear pattern [11–13]. Systemic long-term health risks such as lymphoma or solid cancers of inner organs that would not appear on the tattoo itself are more difficult to detect. Three published case–control studies have investigated the potential hematologic cancer risk associated with tattooing [14–16]. The findings of these studies were mixed, with two observing no increased risk of overall hematologic cancers associated with tattooing, though there was some evidence of increased risk of certain B-cell lymphoma subtypes [15, 16]. Two of these studies were limited by small sample sizes within cancer subtype strata and low tattoo prevalence. The third study, with more than 1300 cases and 4000 controls using Swedish population registry data, found a 20% increased risk for overall lymphoma in tattooed compared with non-tattooed individuals, with the highest risk in the 0–2 years following a first tattoo [14]. Limitations of all three studies included the potential for response bias from the case–control design, limited tattooing exposure data, and not controlling for tattoo-related infections as an alternative pathway in between tattooing and lymphoma.
To overcome these shortcomings, we launched the Cancer Risk Attributable to the Body Art of Tattooing (CRABAT) study, which is embedded in the prospective lifetime cohort Constances and provides a long-term follow-up of cancer (and other chronic conditions) incidence, allowing causal conclusions to be drawn.
Who is in the cohort?
Comprising more than 13 000 tattooed participants and 100 000 non-tattooed controls, CRABAT is, to our knowledge, the largest cohort study on tattoo-associated long-term health effects worldwide. CRABAT is embedded in the large ongoing population-based French cohort study Constances that provides an exhaustive research infrastructure and whose protocol is published [17]. In brief, Constances recruited >200 000 volunteers aged 18–69 years who underwent baseline investigations from late 2012 to 2020. The baseline data were collected through self-administered questionnaires that collected information on demographics and detailed medical histories across 20 different departments of France. Constances’ robust design provides a valuable resource for investigating a wide range of health outcomes and their determinants, including cancers [18].
The CRABAT study is a nested cohort study within Constances. This study applied a two-stage process to assess tattoo exposure. First, basic exposure information was collected via the screening question ‘Do you have a tattoo?’ and a respective tattoo surface (more or fewer than one hand surface) during the annual follow-up in 2020–1. Of the 203 030 Constances participants enrolled during this period, 116 934 (57.6%) responded to the follow-up questionnaire and, of these, 97.9% (n = 114 423) responded to the questionnaire items on tattoos; this latter participant group constitute the eligible CRABAT study population (Figure 1). In the second phase, conducted between July and December 2023, a self-administered tattoo exposure questionnaire, the validated Epidemiological Tattoo Assessment Tool (EpiTAT), was distributed either online or on paper to the previously identified individuals with tattoos, to which the response rate was 60.0% (7965/13 276) [19]. In April 2024, the CRABAT database was created and the tattoo exposure data were merged with selected variables from the Constances data for all participants with known tattoo exposure status. After the exclusion of 37 participants with unknown exposure status (e.g. empty return questionnaires), the total CRABAT population numbered 114 423 participants, of whom 13 239 (11.7%) were tattooed participants, including 7928 (7.0%) participants with full exposure information, and 101 184 (88.4%) were non-tattooed participants.
Figure 1.
Flow chart of sample determination of the CRABAT cohort.
As an asset of the cohort design, individual-level data are available for non-responders to exposure assessment (first and second phases), which helps to evaluate potential selection bias (Table 1). Non-response to the first-phase exposure assessment (tattoo items in the Constances 2020/21 follow-up survey) was mainly influenced by higher participant age. Regarding the second-phase exposure assessment in 2023, while the complexity of the EpiTAT questionnaire could have discouraged some participants from answering, the socio-demographic profiles of respondents differed slightly from those of participants who did not respond. For example, compared with non-respondents, the respondents more often had a university degree, were married or in a civil partnership, and had higher incomes.
Table 1.
Demographic profile of Constances participants answering the follow-up questionnaire in 2020 by respondent status: for tattooed individuals who responded or did not respond to the second-phase exposure questionnaire via the EpiTAT in 2023 (i.e. tattooed participants with full vs partial exposure information), not tattooed individuals, and those who did not respond to the first-phase tattoo assessment in 2020 and whose exposure status is unknown.
| Tattooed |
Not tattooed [n (%)] | No response to tattoo item in 2020 [n (%)] | |||
|---|---|---|---|---|---|
| Factor | EpiTAT responders [n (%)] | No response to EpiTAT [n (%)] | P-value | ||
| Number | 7928 | 5311 | 101 184 | 2511 | |
| Biological sex | <0.001 | ||||
| Male | 2866 (36.2) | 2063 (38.8) | 46 702 (46.2) | 1167 (46.5) | |
| Female | 5062 (63.8) | 3248 (61.2) | 54 482 (53.8) | 1344 (53.5) | |
| Age (years) at first tattoo assessment (follow-up questionnaire 2020) | <0.001 | ||||
| ≤35 | 1639 (20.7) | 1229 (23.1) | 9078 (9.0) | 177 (7.0) | |
| >35 to ≤45 | 2427 (30.6) | 1524 (28.7) | 17 960 (17.7) | 344 (13.7) | |
| >45 to ≤55 | 2159 (27.2) | 1341 (25.2) | 21 706 (21.5) | 485 (19.3) | |
| >55 to ≤65 | 1224 (15.4) | 887 (16.7) | 24 024 (23.7) | 605 (24.1) | |
| >65 | 479 (6.0) | 330 (6.2) | 28 416 (28.1) | 900 (35.8) | |
| Region of birtha | <0.001 | ||||
| France, metropolitan area | 7398 (93.3) | 4801 (90.4) | 91 947 (90.9) | 2259 (90.0) | |
| Europe | 239 (3.0) | 210 (4.0) | 3691 (3.6) | 102 (4.1) | |
| Asia, Africa, French overseas departments | 168 (2.1) | 180 (3.4) | 3712 (3.7) | 87 (3.5) | |
| Other | 123 (1.6) | 120 (2.3) | 1834 (1.8) | 63 (2.5) | |
| Relationship statusa | <0.001 | ||||
| Unmarried (never married) | 2402 (30.3) | 1765 (33.2) | 21 126 (20.9) | 453 (18.0) | |
| Civil partnership | 1314 (16.6) | 797 (15.0) | 11 065 (10.9) | 228 (9.1) | |
| Married | 3125 (39.4) | 2013 (37.9) | 56 067 (55.4) | 1409 (56.1) | |
| Divorced/widowed | 1086 (13.7) | 725 (13.7) | 12 872 (12.7) | 411 (16.4) | |
| Missing | 1 (<1) | 11 (0.2) | 54 (0.1) | 10 (0.4) | |
| Highest educationa | <0.001 | ||||
| No diploma, vocational training | 1719 (21.7) | 1540 (29.0) | 19 998 (19.8) | 751 (29.9) | |
| High-school diploma | 1580 (19.9) | 1187 (22.3) | 14 442 (14.3) | 356 (14.2) | |
| College bachelor’s diploma | 3076 (38.8) | 1737 (32.7) | 36 213 (35.8) | 812 (32.3) | |
| College master’s diploma and higher | 1456 (18.4) | 723 (13.6) | 28 646 (28.3) | 516 (20.5) | |
| Unclassifiable | 15 (0.2) | 12 (0.2) | 239 (0.2) | 10 (0.4) | |
| Missing | 82 (1.0) | 112 (2.1) | 1646 (1.6) | 66 (2.6) | |
| Household disposable incomea (euros) | <0.001 | ||||
| >1500 | 850 (10.7) | 770 (14.5) | 6623 (6.5) | 232 (9.2) | |
| ≥1500 to <2800 | 2224 (28.1) | 1552 (29.2) | 22 882 (22.6) | 666 (26.5) | |
| ≥2800 to <4200 | 2740 (34.6) | 1693 (31.9) | 31 007 (30.6) | 735 (29.3) | |
| ≥4200 | 1614 (20.4) | 852 (16.0) | 34 026 (33.6) | 679 (27.0) | |
| Do not want to answer/missing | 500 (6.3) | 444 (8.4) | 6646 (6.6) | 199 (7.9) | |
| Occupationa | <0.001 | ||||
| Manual worker, semi-skilled worker | 352 (4.4) | 342 (6.4) | 2561 (2.5) | 87 (3.5) | |
| Skilled worker, craftsman, technician | 892 (11.3) | 777 (14.6) | 7381 (7.3) | 289 (11.5) | |
| Office employee, administrative workforce | 1972 (24.9) | 1431 (26.9) | 16 033 (15.8) | 444 (17.7) | |
| Intermediate professions | 2493 (31.4) | 1387 (26.1) | 32 622 (32.2) | 809 (32.2) | |
| Higher executive functions | 606 (7.6) | 339 (6.4) | 17 346 (17.1) | 363 (14.5) | |
| Higher intellectual functions | 1045 (13.2) | 548 (10.3) | 18 190 (18.0) | 291 (11.6) | |
| Has never worked | 152 (1.9) | 115 (2.2) | 1198 (1.2) | 26 (1.0) | |
| Other | 91 (1.1) | 67 (1.3) | 717 (0.7) | 23 (0.9) | |
| Missing | 325 (4.1) | 305 (5.7) | 5136 (5.1) | 179 (7.1) | |
| Smoking statusb | <0.001 | ||||
| Never | 2848 (35.9) | 1616 (30.4) | 50 729 (50.1) | 1158 (46.1) | |
| Former | 3569 (45.0) | 2320 (43.7) | 39 080 (38.6) | 958 (38.2) | |
| Current | 1296 (16.3) | 1182 (22.3) | 8151 (8.1) | 277 (11.0) | |
| Missing | 215 (2.7) | 193 (3.6) | 3224 (3.2) | 118 (4.7) | |
| Alcohol consumptiona | <0.001 | ||||
| Abstinent | 1409 (17.8) | 882 (16.7) | 17 911 (17.7) | 454 (18.1) | |
| Abuse | 678 (8.6) | 581 (11.0) | 7741 (7.7) | 217 (8.6) | |
| Dependence | 478 (6.0) | 347 (6.6) | 5042 (5.0) | 126 (5.0) | |
| Neither abuse nor dependence | 5254 (66.3) | 3352 (63.3) | 68 957 (68.3) | 1639 (65.3) | |
| Missing | 102 (1.3) | 130 (2.5) | 1381 (1.4) | 73 (2.9) | |
| Cannabis consumptionb | <0.001 | ||||
| Never | 3308 (41.7) | 2089 (39.3) | 65 231 (64.5) | 1576 (62.8) | |
| Former | 3651 (46.1) | 2387 (44.9) | 27 150 (26.8) | 627 (25.0) | |
| Current | 751 (9.5) | 603 (11.4) | 3768 (3.7) | 89 (3.5) | |
| Missing | 218 (2.7) | 232 (4.4) | 5035 (5.0) | 219 (8.7) | |
| E-cigarette smoking statusb | <0.001 | ||||
| Never | 4811 (60.7) | 3101 (58.4) | 63 452 (62.7) | 1417 (56.4) | |
| Former | 768 (9.7) | 682 (12.8) | 3887 (3.8) | 132 (5.3) | |
| Current | 491 (6.2) | 409 (7.7) | 2632 (2.6) | 58 (2.3) | |
| Missing | 1858 (23.4) | 1119 (21.1) | 31 213 (30.8) | 904 (36.0) | |
| BMI (kg/m2)a | <0.001 | ||||
| <18 | 139 (1.8) | 108 (2.0) | 1530 (1.5) | 19 (0.8) | |
| 18 to <20 | 776 (9.8) | 433 (8.2) | 8153 (8.1) | 178 (7.1) | |
| 20 to <25 | 3729 (47.0) | 2447 (46.1) | 48 109 (47.5) | 1128 (44.9) | |
| 25 to <30 | 2251 (28.4) | 1553 (29.2) | 30 888 (30.5) | 820 (32.7) | |
| 30+ | 953 (12.0) | 718 (13.5) | 11 442 (11.3) | 345 (13.7) | |
| Missing | 80 (1.0) | 52 (1.0) | 1062 (1.0) | 21 (0.8) | |
| Physical activity outside workb,c | 3.5 (1.5) | 3.4 (1.5) | 3.6 (1.5) | 3.7 (1.5) | <0.001 |
| General healthb,d | 2.6 (1.2) | 2.8 (1.3) | 2.6 (1.2) | 2.8 (1.2) | <0.001 |
Information collected in baseline questionnaire. Missing values replaced with information from follow-up questionnaires until 2020–1.
Information collected from follow-up questionnaire 2020/21. Missing values replaced with information from closest available information from follow-up or baseline information preceding the date of 2020/21 follow-up completion.
Rated on Likert scale from 1 (never) to 7 (daily).
Rated on Likert scale from 1 (perfect) to 8 (very bad).
BMI, body mass index.
How often have they been followed up?
Follow-ups of the Constances cohort are performed annually via self-administered questionnaires, using either paper-based or web-based questionnaires. Of the CRABAT population, 82.5% (n = 94 422) and 75.6% (n = 86 476) also answered the most recent follow-up questionnaires in 2022 and 2023, respectively. The participants are invited for health examination every 5 years; >99% (n = 113 685) of the CRABAT participants were examined at least once and 45.4% (n = 51 944) had at least one more follow-up exam. Individual record linkage to national social health databases (Système national de données de santé, SNDS) has been continuous since 1 January 2007, updated with a 2-year reporting delay, and available for 97.6% (n = 111 698) of CRABAT participants who consented to its usage. Moreover, CRABAT is linked to regional indicators (potential accessibility index for health services and French deprivation index) and to the national old-age insurance fund (Caisse Nationale d’Assurance Vieillesse) that includes, amongst others, data on death certificates. Finally, we plan a second wave of basic exposure assessment in 2030 to eliminate potential non-responder bias induced by low response rates to previous tattoo assessments (i.e. if they systematically differ from responders on key characteristics of exposure such as the size of tattoos) and to catch newly tattooed participants.
What has been measured?
Exposure data were collected by using the validated tattoo exposure questionnaire EpiTAT, which was specifically designed to capture various visual and contextual factors associated with tattoo exposure and is freely accessible [19]. Visual factors included the measurement of tattooed body surface in terms of ‘number of hand surfaces’, both overall and by anatomical location, as well as tattoo colours, degree of tattoo filling (illustrated by visual examples representing 5%, 25%, 50%, 75%, and 100% of the tattoo fill), and tattoo style. Contextual factors included the age of tattoo acquisition by 5-year age groups, the circumstances of tattoo acquisition, and whether tattoos were obtained outside of France, with respective countries recorded if applicable. Additionally, the questionnaire assessed tattoo complications, whether associated with issues such as poor wound healing or aftercare, and information related to tattoo removal.
Using the collected exposure data, different exposure metrics were derived, including total tattooed body surface area, overall and stratified for anatomical location in hand palms and cm2 (hand surface area estimated in cm2 according to Sacco et al.) [20]. Subsequently, the total ink exposure was estimated by adjusting the tattooed surface area by multiplication with the proportion of tattoo filling (e.g. in the case of 25% filling, the tattooed body surface was multiplied by 0.25).
Additional self-reported data
Socio-demographic and lifestyle data available include age in years, sex at birth (male/female), education and income level, marital status, origin, household average net income and occupational status, body mass index (BMI), alcohol consumption, cigarette smoking, e-cigarette consumption, and cannabis consumption. Furthermore, data on co-exposures including metals, fumes, solvents, pesticides, and chemical cleaning products can be included in cancer risk analyses. Supplementary Table S1 shows a non-exhaustive list of these additional socio-demographic, lifestyle, environmental, and medical variables available through Constances. Because many of these variables are collected repeatedly, changes in lifestyle and other confounders over time can be considered through these regular cohort follow-up examinations.
Medical data
As part of Constances, objective medical data are available through SNDS, covering information on the following medical acts received and (fully and partially) reimbursed by the French National Health Insurance, including: (i) visits to private practices and type of practitioner and care received; (ii) hospitalizations including ICD-10 diagnosis, duration of stay, and care received; (iii) suffering from chronic disease (Affection Long Durée) including disease onset and care received; and (iv) drug prescriptions. Medical data are complemented by self-reported medical questionnaires: at baseline, current physical and psychological wellbeing and lifetime medical history were assessed. Annual follow-ups capture additional medical information such as physical and psychological wellbeing, as well as the occurrence of specific diagnoses including incident cancer diagnoses during the 12 months prior. Furthermore, individual case verification via the re-contact of incidence cancer cases can provide more detailed information on cancer diagnosis.
What has it found?
Table 2 shows various demographic and lifestyle characteristics of individuals categorized by tattoo status and sex. Overall, the study population consists of more women than men, and even more so among those with tattoos [women: n = 8310 (62.8%); men: n = 4929 (37.2%)]. While, in the total cohort, the older age groups predominate, tattoo prevalence decreases with increasing age, leading to an inverse age/tattoo distribution within the cohort. Among both sexes, tattooed individuals had a higher prevalence of substance use compared with non-tattooed individuals. Current smoking status, alcohol abuse and dependence, ever use of e-cigarettes, and ever use of cannabis were all higher in tattooed compared with non-tattooed individuals. Patterns were similar when stratified by sex. Tattooed compared with non-tattooed individuals also had slightly higher percentages of underweight and obese groups, had a lower income, were lower educated, and were less often in a romantic relationship.
Table 2.
Socio-demographic profile of the CRABAT cohort population by tattoo status and sex.
| Not tattooed (n = 101 184) |
Tattooed (n = 13 239) |
||||
|---|---|---|---|---|---|
| Factor | Overall [n (%)] | Men [n (%)] | Women [n (%)] | Men [n (%)] | Women [n (%)] |
| Total number (%) | 114 423 (10) | 46 702 (46.1) | 54 482 (53.8) | 4929 (37.2) | 8310 (62.8) |
| Age (years) at first tattoo assessment (follow-up questionnaire 2020) | |||||
| ≤35 | 11 946 (10.4) | 4161 (8.9) | 4917 (9.0) | 689 (14.0) | 2179 (26.2) |
| >35 to ≤45 | 21 911 (19.1) | 8356 (17.9) | 9604 (17.6) | 1274 (25.8) | 2677 (32.2) |
| >45 to ≤55 | 25 206 (22.0) | 9858 (21.1) | 11 848 (21.7) | 1391 (28.2) | 2109 (25.4) |
| >55 to ≤65 | 26 135 (22.8) | 10 679 (22.9) | 13 345 (24.5) | 1112 (22.6) | 999 (12.0) |
| >65 | 29 225 (25.5) | 13 648 (29.2) | 14 768 (27.1) | 463 (9.4) | 346 (4.2) |
| Region of birtha | |||||
| France, metropolitan area | 104 146 (91.0) | 42 365 (90.7) | 49 582 (91.0) | 4494 (91.2) | 7705 (92.7) |
| Europe | 4140 (3.6) | 1685 (3.6) | 2006 (3.7) | 185 (3.8) | 264 (3.2) |
| Asia, Africa, French oversea departments | 4060 (3.5) | 1892 (4.1) | 1820 (3.3) | 152 (3.1) | 196 (2.4) |
| Other | 2077 (1.8) | 760 (1.6) | 1074 (2.0) | 98 (2.0) | 145 (1.7) |
| Relationship statusa | |||||
| Unmarried (never married) | 25 293 (22.1) | 9644 (20.7) | 11 482 (21.1) | 1290 (26.2) | 2877 (34.6) |
| Civil partnership | 13 176 (11.5) | 5562 (11.9) | 5503 (10.1) | 762 (15.5) | 1349 (16.2) |
| Married | 61 205 (53.5) | 26 914 (57.6) | 29 153 (53.5) | 2217 (45.0) | 2921 (35.2) |
| Divorced/widowed | 14 683 (12.8) | 4556 (9.8) | 8316 (15.3) | 655 (13.3) | 1156 (13.9) |
| Missing | 66 (0.1) | 26 (0.1) | 28 (0.1) | 5 (0.1) | 7 (0.1) |
| Highest educationa | |||||
| No diploma, vocational training | 23 257 (20.3) | 10 166 (21.8) | 9832 (18.0) | 1724 (35.0) | 1535 (18.5) |
| High-school diploma | 17 209 (15.0) | 6458 (13.8) | 7984 (14.7) | 933 (18.9) | 1834 (22.1) |
| College bachelor’s diploma | 41 026 (35.9) | 14 091 (30.2) | 22 122 (40.6) | 1451 (29.4) | 3362 (40.5) |
| College master’s diploma and higher | 30 825 (26.9) | 15 072 (32.3) | 13 574 (24.9) | 720 (14.6) | 1459 (17.6) |
| Unclassifiable | 266 (0.2) | 112 (0.2) | 127 (0.2) | 6 (0.1) | 21 (0.3) |
| Missing | 1840 (1.6) | 803 (1.7) | 843 (1.5) | 95 (1.9) | 99 (1.2) |
| Household disposable income (euros)a | |||||
| >1500 | 8243 (7.2) | 2658 (5.7) | 3965 (7.3) | 478 (9.7) | 1142 (13.7) |
| ≥1500 to <2800 | 26 658 (23.3) | 9516 (20.4) | 13 366 (24.5) | 1349 (27.4) | 2427 (29.2) |
| ≥2800 to <4200 | 35 440 (31.0) | 14 496 (31.0) | 16 511 (30.3) | 1758 (35.7) | 2675 (32.2) |
| ≥4200 | 36 492 (31.9) | 17 486 (37.4) | 16 540 (30.4) | 1046 (21.2) | 1420 (17.1) |
| Do not want to answer/missing | 7590 (6.6) | 2546 (5.5) | 4100 (7.5) | 298 (6.0) | 646 (7.8) |
| Occupationa | |||||
| Manual worker, semi-skilled worker | 3255 (2.8) | 1572 (3.4) | 989 (1.8) | 381 (7.7) | 313 (3.8) |
| Skilled worker, craftsman, technician | 9050 (7.9) | 5337 (11.4) | 2044 (3.8) | 1091 (22.1) | 578 (7.0) |
| Office employee, administrative workforce | 19 436 (17.0) | 3189 (6.8) | 12 844 (23.6) | 532 (10.8) | 2871 (34.5) |
| Intermediate professions | 36 502 (31.9) | 12 862 (27.5) | 19 760 (36.3) | 1389 (28.2) | 2491 (30.0) |
| Higher executive functions | 18 291 (16.0) | 9527 (20.4) | 7819 (14.4) | 377 (7.6) | 568 (6.8) |
| Higher intellectual functions | 19 783 (17.3) | 11 215 (24.0) | 6975 (12.8) | 809 (16.4) | 784 (9.4) |
| Has never worked | 1465 (1.3) | 414 (0.9) | 784 (1.4) | 38 (0.8) | 229 (2.8) |
| Other | 875 (0.8) | 316 (0.7) | 401 (0.7) | 47 (1.0) | 111 (1.3) |
| Missing | 5766 (5.0) | 2270 (4.9) | 2866 (5.3) | 265 (5.4) | 365 (4.4) |
| Smoking statusb | |||||
| Never | 55 193 (48.2) | 21 014 (45.0) | 29 715 (54.5) | 1375 (27.9) | 3089 (37.2) |
| Former | 44 969 (39.3) | 20 361 (43.6) | 18 719 (34.4) | 2454 (49.8) | 3435 (41.3) |
| Current | 10 629 (9.3) | 3835 (8.2) | 4316 (7.9) | 936 (19.0) | 1542 (18.6) |
| Missing | 3632 (3.2) | 1492 (3.2) | 1732 (3.2) | 164 (3.3) | 244 (2.9) |
| Alcohol consumptiona | |||||
| Abstinent | 20 202 (17.7) | 5651 (12.1) | 12 260 (22.5) | 560 (11.4) | 1731 (20.9) |
| Abuse | 9000 (7.9) | 5426 (11.6) | 2315 (4.3) | 712 (14.5) | 547 (6.6) |
| Dependence | 5867 (5.1) | 1986 (4.3) | 3056 (5.6) | 260 (5.3) | 565 (6.8) |
| Neither abuse nor dependence | 77 563 (67.9) | 33 056 (70.9) | 35 901 (66.0) | 3315 (67.5) | 5291 (63.8) |
| Missing | 1613 (1.4) | 517 (1.1) | 864 (1.6) | 67 (1.4) | 165 (2.0) |
| Cannabis consumptionb | |||||
| Never | 70 628 (61.7) | 27 947 (59.8) | 37 284 (68.4) | 1753 (35.6) | 3644 (43.9) |
| Former | 33 188 (29.0) | 14 426 (30.9) | 12 724 (23.4) | 2338 (47.4) | 3700 (44.5) |
| Current | 5122 (4.5) | 2460 (5.3) | 1308 (2.4) | 652 (13.2) | 702 (8.4) |
| Missing | 5485 (4.8) | 1869 (4.0) | 3166 (5.8) | 186 (3.8) | 264 (3.2) |
| E-cigarette smoking statusb | |||||
| Never | 71 364 (62.4) | 29 618 (63.4) | 33 834 (62.1) | 2899 (58.8) | 5013 (60.3) |
| Former | 5337 (4.7) | 1985 (4.3) | 1902 (3.5) | 568 (11.5) | 882 (10.6) |
| Current | 3532 (3.1) | 1380 (3.0) | 1252 (2.3) | 372 (7.5) | 528 (6.4) |
| Missing | 34 190 (29.9) | 13 719 (29.4) | 17 494 (32.1) | 1090 (22.1) | 1887 (22.7) |
| BMI (kg/m2)a | |||||
| <18 | 1777 (1.6) | 270 (0.6) | 1260 (2.3) | 24 (0.5) | 223 (2.7) |
| 18 to <20 | 9362 (8.2) | 1736 (3.7) | 6417 (11.8) | 168 (3.4) | 1041 (12.5) |
| 20 to <25 | 54 285 (47.4) | 20 805 (44.5) | 27 304 (50.1) | 2100 (42.6) | 4076 (49.0) |
| 25 to <30 | 34 692 (30.3) | 17 935 (38.4) | 12 953 (23.8) | 1903 (38.6) | 1901 (22.9) |
| 30+ | 13 113 (11.5) | 5476 (11.7) | 5966 (11.0) | 685 (13.9) | 986 (11.9) |
| Missing | 1194 (1.0) | 480 (1.0) | 582 (1.1) | 49 (1.0) | 83 (1.0) |
| Physical activity outside workb,c | 3.6 (1.5) | 3.6 (1.5) | 3.7 (1.5) | 3.5 (1.5) | 3.4 (1.5) |
| General healthb,d | 2.6 (1.2) | 2.6 (1.2) | 2.7 (1.2) | 2.7 (1.2) | 2.7 (1.3) |
Information collected in baseline questionnaire. Missing values replaced with information from follow-up questionnaires until 2020–1.
Information collected from follow-up questionnaire 2020/1. Missing values replaced with information from closest available information from follow-up or baseline information preceding the date of 2020–1 follow-up completion.
Rated on Likert scale from 1 (never) to 7 (daily).
Rated on Likert scale from 1 (perfect) to 8 (very bad).
To assess tattooing-incurred cancer risk, profound knowledge on the exposure is key: tattoo pigment toxicity varies by tattoo colour and, due to different forms and shapes of tattoos, the tattoo ‘filling’ needs to be considered when estimating the total tattooed body surface. Furthermore, the time since tattooing defines the assumed lag-time to cancer formation and the tattoo circumstances give information about potential infections related to tattooing that potentially mediate the tattoo–cancer relationship. Table 3 provides a comprehensive overview of tattoo characteristics for individuals with known exposure details. The median tattoo size of tattooed participants was one hand surface (interquartile range = 0.5; 3.0) and translated to a metric median of 151.7 cm2 (67.2; 377.0). Tattoo size was the highest in younger age groups and in men (Figure 2). By adjusting for tattoo filling, the median tattoo surface area among the overall tattooed participants decreased to nearly half its original size. Most participants had only small tattoos, whilst one-fifth had mostly or only large tattoos. Females were more likely to prefer small and men more likely to prefer large tattoos.
Table 3.
Tattoo characteristics among CRABAT cohort participants with tattoos and full exposure information assessed via the EpiTAT in the second-phase exposure assessment in 2023.
| Number of complete observations | Tattooed (n = 7928) | Women (n = 5062) | Men (n = 2866) | P-value | |
|---|---|---|---|---|---|
| Tattooed body surface [median (IQRa)] | |||||
| In hand surfaces | 7922 | 1 (0.5–3.0) | 1.0 (0.5–2.8) | 1.0 (0.5–3.0) | <0.001 |
| Transformed to cm2 | 7768 | 151.7 (67.2–377.0) | 133.5 (60.1–326.4) | 174.34 (82.1–492.4) | <0.001 |
| Adjusted for tattoo filling (cm2) | 7678 | 74.6 (28.3–196.3) | 63.4 (24.5–160.2) | 106.0 (38.5–282.2) | <0.001 |
| Pattern of tattoo sizes [n (%)] | 7830 | <0.001 | |||
| Only small tattoos | 4236 (54.1) | 2890 (57.8) | 1346 (47.5) | ||
| Mostly small tattoos | 1020 (13.0) | 765 (15.3) | 255 (9.0) | ||
| Small and large tattoos | 736 (9.4) | 501 (10.0) | 235 (8.3) | ||
| Mostly large tattoos | 714 (9.1) | 366 (7.3) | 348 (12.3) | ||
| Only large tattoos | 989 (12.6) | 411 (8.2) | 578 (20.4) | ||
| Tattoo location [n (%)] | 7910 | ||||
| Tattooed on head/neck | 188 (2.4) | 169 (3.3) | 19 (0.7) | <0.001 | |
| Arms and shoulders | 5283 (66.8) | 3075 (60.9) | 2208 (77.1) | <0.001 | |
| Trunk | 3781 (47.8) | 2690 (53.3) | 1091 (38.1) | <0.001 | |
| Legs and butt | 2883 (36.4) | 2149 (42.6) | 734 (25.6) | <0.001 | |
| Genital zone | 107 (1.4) | 82 (1.6) | 25 (0.9) | 0.005 | |
| Tattoo colour [n (%)] | 7897 | ||||
| Black/grey | 7088 (89.8) | 4631 (91.9) | 2457 (85.9) | <0.001 | |
| Dark blue/dark green | 1769 (22.4) | 1040 (20.6) | 729 (25.5) | <0.001 | |
| Light blue/light green | 1293 (16.4) | 834 (16.6) | 459 (16.0) | 0.56 | |
| Yellow/orange | 1143 (14.5) | 712 (14.1) | 431 (15.1) | 0.26 | |
| Bright red | 1531 (19.4) | 912 (18.1) | 619 (21.6) | <0.001 | |
| Brown/dark red | 503 (6.4) | 309 (6.1) | 194 (6.8) | 0.26 | |
| Rose/pink/violet | 855 (10.8) | 677 (13.4) | 178 (6.2) | <0.001 | |
| White | 880 (11.1) | 565 (11.2) | 315 (11.0) | 0.78 | |
| Other | 115 (1.5) | 65 (1.3) | 50 (1.7) | 0.10 | |
| Tattoo context [n (%)] | 7864 | ||||
| Tattooed by professional artist in a studio | 6968 (88.6) | 4608 (91.9) | 2360 (82.8) | <0.001 | |
| Tattooed by professional artist outside studio | 908 (11.5) | 577 (11.5) | 331 (11.6) | 0.89 | |
| Tattooed by lay person | 288 (3.7) | 119 (2.4) | 169 (5.9) | <0.001 | |
| Tattooed hand-poked or with traditional techniques | 361 (4.6) | 126 (2.5) | 235 (8.2) | <0.001 | |
| Tattooed in other context | 163 (2.1) | 86 (1.7) | 77 (2.7) | 0.003 | |
| Tattoo period (years ago) [n (%)] | 7859 | ||||
| Within last year | 923 (11.7) | 685 (13.7) | 238 (8.4) | <0.001 | |
| 1 to ≤5 | 2521 (32.1) | 1778 (35.5) | 743 (26.1) | <0.001 | |
| 5 to ≤10 | 2686 (34.2) | 1828 (36.5) | 858 (30.1) | <0.001 | |
| 10 to ≤15 | 1904 (24.2) | 1252 (25.0) | 652 (22.9) | 0.036 | |
| 15+ | 3628 (46.2) | 2156 (43.0) | 1472 (51.7) | <0.001 | |
| Tattooed outside home country [n (%)] | 7861 | 1218 (15.5) | 748 (14.9) | 470 (16.5) | 0.064 |
| Any piercing [n (%)] | 7854 | 2316 (29.5) | 1995 (39.9) | 321 (11.3) | <0.001 |
| Ever any adverse reaction [n (%)] | 7856 | 342 (4.4) | 237 (4.7) | 105 (3.7) | 0.028 |
| Ever had tattoo removed [n (%)] | 7849 | 131 (1.7) | 78 (1.6) | 53 (1.9) | 0.32 |
| Are tattoos protected from sun? [n (%)] | <0.001 | ||||
| No | 2272 | 2272 (28.9) | 1192 (23.8) | 1080 (37.9) | |
| Yes, always | 1962 | 1962 (25.0) | 1429 (28.6) | 533 (18.7) | |
| Yes, sometimes | 2872 | 2872 (36.6) | 1910 (38.2) | 962 (33.8) | |
| None of my tattoos is sun-exposed | 700 | 700 (8.9) | 449 (9.0) | 251 (8.8) | |
| I don’t know | 45 | 45 (0.6) | 21 (0.4) | 24 (0.8) |
aIQR, interquartile range.
Figure 2.
Bar plot of median and interquartile ranges of self-estimated tattoo surface, converted to cm2, by biological sex and stratified by age bands in the tattooed subsample with full exposure information of the CRABAT cohort.
The location of tattoos varied by sex. The arm and shoulder region was the most commonly tattooed area, with 60.9% of females and 77.1% of males having tattoos on this body part. This was followed by the trunk and the legs, both more often tattooed by women compared with men. By far the most common tattoo colour, reported by 89.8% of participants, was black/grey. Other common colours reported by 15%–20% of tattooed participants were dark blue/green, bright red, light blue/green, and yellow/orange. Almost 9 in 10 tattooed individuals received at least one tattoo in professional studios, while more than 1 in 10 also were tattooed outside of studios by professionals. Tattoos acquired from lay persons and through traditional ‘hand-poked’ techniques were more common in men compared with women. Almost half of participants have tattoos for >15 years, with a higher proportion of males than females, whilst more than 1 out of 10 tattooed individuals reported a new tattoo during the last year. About 15% reported being tattooed outside France, with most participants being tattooed in neighbouring countries, Asia (Thailand), or North America (data not shown). One-third of participants had piercings and adverse effects from tattooing were reported by <5% of the tattooed population and slightly more often by women compared to men. Less than 2% underwent tattoos removal; thereby, the removed tattoo surface was usually less than the area of a hand and >10% reported adverse effects of removal (data not shown). Finally, while most participants protected their tattoos from the sun at least sometimes, 38% of men reported not doing so.
These data will allow us to estimate tattoo-incurred cancer risks in prospective analysis designs in 5-year intervals starting in 2027. In the first cross-sectional analysis, we used these excellent data to analyse hepatitis risk associated with tattooing. Excluding injection drug users and participants living with HIV, and adjusting our analyses for homosexual intercourse amongst men, number of sexual partners, condom use, alcohol use, cannabis use, and educational status, we found unsafe tattooing practices outside tattoo studios associated with an over four times elevated risk for hepatitis (manuscript under review).
What are the main strengths and weaknesses?
As a main strength, CRABAT is nested in the French national Constances cohort, providing rich complementary data and allowing long-term prospective follow-up. Its individual record linkage to the national health insurance assures objective health outcome data. In using the Constances infrastructure, CRABAT can address a multitude of research questions in selecting for each of them separately from the relevant outcome and covariate data from the study variable pool. The strong socio-demographic differences of the tattooed compared with the non-tattooed study population, particularly for known cancer risk factors, underscore the importance of the available complementary data to consider in risk analyses. Moreover, the exceptional tattoo exposure data that were collected by using a validated questionnaire in almost 8000 participants will allow the assessment of dose–response relationships and stratification on visual or contextual tattoo factors.
As a weakness of many cohort studies, CRABAT relies on self-reported data. As an example, the validation study of the EpiTAT exposure questionnaire showed that the self-reported tattoo size is strongly overestimated [19]. Visual mobile-phone-assisted tattoo surface measures might become a more accurate alternative but were not available at the time of data collection. Finally, owing to its design and presenting both an asset and a limitation at once, the Constances cohort population is a particularly healthy population, as it consists of volunteers who agreed to participate in a life-long cohort. We can assume that the tattoo prevalence in the general population is higher than in Constances and that known cancer risk factors (e.g. smoking, alcohol abuse) could be more common in the general population than in Constances. While this may reduce the generalizability of the (tattooed) Constances population for the (tattooed) population as a whole, the estimation of cancer risks might be less influenced by confounding factors that predominantly affect tattooed individuals.
Can I get hold of the data? Where can I find out more?
Due to European data-protection regulations, the CRABAT data cannot be made available to the public. In accordance with the Constances Charter, de-identified participant data from the Constances cohort are available to researchers who meet the legal and ethical requirements set by the French National Commission governing data-privacy laws. International researchers can access the dataset at https://www.constances.fr/en/scientific-area/access-to-constances-2/. Additionally, all study materials, including the study protocol and data dictionary of the Constances cohort, are freely accessible. For more information on data usage and interest in research collaboration, please contact Milena Foerster (foersterm@iarc.who.int).
Ethics approval
The CRABAT study received additional approval by the IARC Ethics Committee (IEC 22–02) and was authorized by the French Data Protection Authority (Commission Nationale de l’Informatique et des Libertés, CNIL; Authorization #22015584). The Constances study was approved by the Institutional Review Board of the French Institute of Health (Institut national de la santé et de la recherche médicale, Inserm) (Opinion n°01–011, then n°21–842) and authorized by the by the CNIL (Authorization #910486).
Supplementary Material
Contributor Information
Bayan Hosseini, Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Rachel McCarty, Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Marie Zins, Population-Based Cohorts Unit, INSERM UMS 11, Paris Saclay University, Paris, France.
Marcel Goldberg, Population-Based Cohorts Unit, INSERM UMS 11, Paris Saclay University, Paris, France.
Céline Ribet, Population-Based Cohorts Unit, INSERM UMS 11, Paris Saclay University, Paris, France.
Ines Schreiver, Dermatotoxicology Study Centre, Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Khaled Ezzedine, Department of Dermatology, University Hospital Henri-Mondor, University of Paris Est-Créteil, Créteil, France.
Joachim Schüz, Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Milena Foerster, Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Author contributions
M.F., J.S., and I.S. made substantial contributions to the design and conduct of the work. B.H., R.D.M., and M.F. drafted and prepared the manuscript. B.H. and M.F. conducted the statistical analyses. The manuscript was reviewed and commented on by all authors. The final version of the manuscript has been approved by all authors. The remaining authors (M.Z., M.G., S.K., C.R., K.E.) contributed to the main cohort study and ensured the quality of the original data and that questions related to the accuracy of any part of the work were appropriately investigated. All authors contributed to the interpretation of the results, revising it critically for important intellectual content. M.F., contributors as being responsible for the overall content as guarantors. The work reported in the paper has been performed by the authors, unless clearly specified in the text. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.
Conflict of interest: None declared.
Funding
The CRABAT study was supported by the French National Cancer Institute (Institut National du Cancer, INCa; grant no. 2021–137). None of these funding sources had any role in the design of the study, collection and analysis of data, or decision to publish. The Constances cohort study was supported and funded by the French National Health Insurance fund (Caisse nationale d’assurance maladie, Cnam). Constances is a national infrastructure for biology and health (‘Infrastructure nationale en biologie et santé’) and benefits from a grant from the French national agency for research (Agence National de Recherche, ANR-11-INBS-0002). Constances is also partly funded to a small extent by industrial companies, notably in the healthcare sector, within the framework of Public-Private Partnerships.
Use of artificial intelligence (AI)
No AI was used, either to conduct the study or in preparing this article. All authors certify that the above paper, including the displayed tables and figures, is an outcome of our independent and original work. We have given proper acknowledgement to all the sources from which the ideas and extracts have been taken.
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