- Email: [email protected]
Cigarette smoking and risk of Hodgkin lymphoma and its subtypes: a pooled analysis from the International Lymphoma Epidemiology Consortium (InterLymph)
Annals of Oncology
reviews Annals of Oncology 24: 2245–2255, 2013 doi:10.1093/annonc/mdt218 Published online 19 June 2013
Cigarette smoking and risk of Hodgkin lymphoma and its subtypes: a pooled analysis from the International Lymphoma Epidemiology Consortium (InterLymph) M. Kamper-Jørgensen1,2*, K. Rostgaard2, S. L. Glaser3,9, S. H. Zahm4, W. Cozen5, K. E. Smedby6, S. Sanjosé7, E. T. Chang8,9, T. Zheng25, C. La Vecchia11,12, D. Serraino13, A. Monnereau14,15, E. V. Kane16, L. Miligi17, P. Vineis18, J. J. Spinelli19,20, J. R. McLaughlin21,22, P. Pahwa23,24, J. A. Dosman23, M. Vornanen33, L. Foretova10, M. Maynadie29, A. Staines, N. Becker26, A. Nieters28, P. Brennan30, P. Boffetta31,32, P. Cocco27 & H. Hjalgrim2 1
Department of Public Health, University of Copenhagen, Copenhagen; 2Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark; Department of Research, Cancer Prevention Institute of California, Fremont; 4Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville; 5 Department of Preventive Medicine and Pathology, University of Southern California, Los Angeles, USA; 6Unit of Clinical Epidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; 7Unit of Infections and Cancer, Institut Catalá d’Oncologia, Barcelona, Spain; 8Health Sciences Practice, Exponent, Inc., Menlo Park; 9 Department of Health Research and Policy, Stanford University School of Medicine, Stanford, USA; 10Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic; 11Department of Epidemiology, Istituto di Ricerche Farmacologiche ‘Mario Negri’ IRCCS; 12Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan; 13Epidemiology and Biostatistics Unit, Scientific Directorate, IRCCS Centro Riferimento Oncologico, Aviano, Italy; 14Environmental Epidemiology of Cancer Group, Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018 Villejuif; 15 Registry of Hematological Malignancies in Gironde, Department of Clinical Research and Medical Information, Bergonié Institute, Bordeaux, France; 16Department of Health Sciences, University of York, Yorkshire, UK; 17Environmental and Occupational Epidemiology Unit, ISPO-Cancer Prevention and Research Institute, Florence, Italy; 18 Environmental Epidemiology, Imperial College London, London, UK; 19BC Cancer Agency, Cancer Control Research, Vancouver; 20School of Population and Public Health, University of British Columbia, Vancouver; 21Dalla Lana School of Public Health, University of Toronto, Toronto; 22Samuel Lunenfeld Research Institute, Mount Sinai Hospital Joseph and Wolf Lebovic Health Complex, Toronto; 23Canadian Centre for Health and Safety in Agriculture; 24Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada; 25Department of Epidemiology and Public Health, Yale University, New Haven, USA; 26Department of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; 27Department of Public Health, University of Cagliari, Monserrato, Italy; 28Department of Cancer Epidemiology and Genetics, Universitätsklinikum Freiburg, Germany; 29Registre des Hémopathies Malignes de Côte d’Or (EA4184), University of Burgundy and University Hospital, Dijon; 30Genetics Section, International Agency for Research on Cancer, Lyon, France; 31The Tisch Cancer Institute and Institute for Translational Epidemiology, Mount Sinai School of Medicine, New York, USA; 32International Prevention Research Institute, Lyon, France; 33Department of Pathology, Fimlab Laboratories, Tampere, Finland 3
Background: The etiology of Hodgkin lymphoma (HL) remains incompletely characterized. Studies of the association between smoking and HL have yielded ambiguous results, possibly due to differences between HL subtypes. Patients and methods: Through the InterLymph Consortium, 12 case–control studies regarding cigarette smoking and HL were identified. Pooled analyses on the association between smoking and HL stratified by tumor histology and Epstein–Barr virus (EBV) status were conducted using random effects models adjusted for confounders. Analyses included 3335 HL cases and 14 278 controls. Results: Overall, 54.5% of cases and 57.4% of controls were ever cigarette smokers. Compared with never smokers, ever smokers had an odds ratio (OR) of HL of 1.10 [95% confidence interval (CI) 1.01–1.21]. This increased risk reflected associations with mixed cellularity cHL (OR = 1.60, 95% CI 1.29–1.99) and EBV-positive cHL (OR = 1.81, 95% CI 1.27– 2.56) among current smokers, whereas risk of nodular sclerosis (OR = 1.09, 95% CI 0.90–1.32) and EBV-negative HL (OR = 1.02, 95% CI 0.72–1.44) was not increased. Conclusion: These results support the notion of etiologic heterogeneity between HL subtypes, highlighting the need for HL stratification in future studies. Even if not relevant to all subtypes, our study emphasizes that cigarette smoking should be added to the few modifiable HL risk factors identified. Key words: Hodgkin lymphoma, case–control, cigarette smoking, epidemiology, Epstein–Barr virus, individual patient data meta-analysis
*Correspondence to: Dr Mads Kamper-Jørgensen, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Postbox 2099, DK-1014 Copenhagen K, Denmark, Tel: +45-35327391; Fax: +45-35351181; E-mail [email protected]
© The Author 2013. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected].
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Received 15 December 2012; revised 26 April 2013; accepted 2 May 2013
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introduction
| Kamper-Jørgensen et al.
countries, epidemiological investigations have often suffered from limited statistical power to detect risk factors for HL overall, and even more so for HL subtypes. To address this limitation, in the present investigation, we took advantage of an international collaboration across 12 case–control studies totaling more than 3300 patients with HL and more than 14,000 controls without HL with individual level exposure data, information about HL histology and, in a subset, HL EBV status.
materials and methods Candidate studies were identified through the InterLymph Consortium [29]. Original case–control studies with electronically available data and information regarding cigarette smoking and HL were included in the study pool. From studies with relevant data, we also requested information on tumor histology and EBV status. Smoking history was assessed using the following variables: ever versus never tobacco smoking, type of tobacco smoking (cigarettes, other, or both); and for cigarette smokers, current or former smoking status, age at initiation, average number of cigarettes smoked per day, duration of smoking, and time since cessation. In an included study of twins [30], the interview data were obtained many years after HL diagnosis. The smoking variables, age, and time were transformed to reflect the situation at the time of diagnosis of the case twin. Smoking variables from individual studies were harmonized to fit common definitions of ‘ever cigarette smoker’, ‘current cigarette smoker’, and ‘former cigarette smoker’. These common definitions aimed at establishing the smoking status at the time of diagnosis in cases, rather than at the time of interview. Former cigarette smoking implied cessation for such a long time (≥1 year) that it was not primarily a matter of reverse causation. However, two American studies had information on time since cessation only for a small fraction of former smokers, making this correction ineffective in these cases. Forty-nine cases and 30 controls were recoded from former to current smokers to fulfill this requirement. We defined participants as ever cigarette smokers based on the criteria available in each study. Inclusion in this study required the participant to either have a cigarettesmoking history or to have never-smoked tobacco. The duration of cigarette smoking was defined by the individual studies, and pack-years were calculated as the product of the duration of cigarette smoking and cigarette-smoking intensity. Categorical analyses for initiation age, cigarettes per day, smoking duration, and pack years were carried out on current compared with never cigarette smokers. Dose–response analyses regarding years since cessation were carried out on the group of former compared with never cigarette smokers. Dose–response trend analyses were restricted to either current or former cigarette smokers as appropriate. Besides HL and cigarette-smoking-related data, we requested interview data on age at diagnosis (or pseudo-diagnosis for controls), sex, calendar year, locality, race (White, Black, Asian, other), current and childhood socioeconomic status (each coded as 3: low, 2: mid, 1: high), pre-school childcare attendance,
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Despite considerable efforts, the etiology of classical Hodgkin lymphoma (cHL) remains incompletely characterized with few risk factors firmly established [1]. Because cHL is among the most common malignancies among adolescents and younger adults in industrialized countries in the Western hemisphere [2], and because high cure rates have hitherto been accompanied by considerable treatment-related morbidity among survivors, research on risk factors is warranted. The uncertainty regarding risk factors may reflect that cHL comprises a number of etiologically diverse entities [3, 4], which have been difficult to define precisely. Accordingly, cHL can be grouped according to age at diagnosis (children, younger adults, older adults), histological subtype (nodular sclerosis, mixed cellularity, lymphocyte rich, lymphocyte depleted), and the presence/absence of Epstein–Barr virus (EBV) in the malignant Hodgkin/Reed-Sternberg cells [3–6]. While these criteria are correlated, e.g. the predominant type of cHL in younger adults is nodular sclerosis, the majority of which is EBV negative, they are at the same time too incongruent to be mutually interchangeable. Therefore, the interpretation and comparison of epidemiological studies employing different stratifications of cHL and including different proportions of each subtype in the patient population can be difficult. Thus, studies are warranted which may lead to the definition of etiologically distinct cHL subtypes, e.g. by identifying subtype-specific risk factors. Tobacco smoking is a potential cHL risk factor worth scrutinizing. Though the number of studies is relatively limited, findings have been inconsistent, e.g. both statistically significantly reduced [7], statistically unchanged [8–19] and statistically significantly increased [20–26] risks of HL overall have been reported among ever smokers. The inconsistent findings may reflect various methodological issues, such as limited statistical power because of small sample sizes, biases related to choice of comparison groups, study participation, exposure assessment, and uncontrolled confounding. However, it may also reflect uneven distributions of cHL variants between the studies, as described above. For example Briggs et al. observed a 1.3-fold significantly increased risk of HL overall among ever versus never smokers [20]. The increased HL risk appeared to be restricted to current smokers, among whom the association was considerably stronger for mixed cellularity HL [odds ratio (OR) = 3.4, 95% confidence interval (CI) 1.8–6.4] than for the nodular sclerosis HL variant (OR = 1.6, 95% CI 1.1–2.3). Two recent meta-analyses reached the conclusion that cigarette smoking increases the overall risk of HL [27, 28]. One meta-analysis of 14 case–control studies and 3 cohort studies reported a stronger association of current smoking with risk of EBV-positive HL (summary relative risk [RR] = 2.26) than EBV-negative HL (summary RR = 1.40) [27]. The other metaanalysis, which included 17 case–control studies and 4 cohort studies of HL, reported a stronger association of current smoking with risk of mixed cellularity HL (summary RR = 2.53) than nodular sclerosis HL (summary RR = 1.35) [28]. Neither of the two, however, simultaneously considered tumor histology and EBV status. Because HL is a relatively rare disease with annual incidence rates around 2–4 per 100 000 person-years in Western
Annals of Oncology
Data provider (reference)
Location
Period of interview
Age at (pseudo) diagnosis
Cases (% part)
Controls (% part)
Matching
Source of controls
Chang [22]
Massachusetts, Connecticut (USA)
1998–2002
15–81
500 (85)
614 (56)
Cozen [30, 59] Dal Maso [31, 60] EpiLymph [24]
USA and Canada Aviano, Milan (Italy) Aviano, Naples (Italy) Spain, France, Italy, Germany, Ireland, Finland and Czech Republic
1961–1992 1983–1992 1998–2002 1998–2004
14–49 14–85 18–83 16–100
86 (70) 156 (97) 62 (97) 324 (88)
87 (70) 1,149 (97) 499 (91) 2,511 (81)
Population controls without a prior history of Hodgkin’s lymphoma Twin controls Hospital controls admitted for acute, nonneoplastic, nonimmunological conditions Controls without cancer drawn from population registers (Italy and Germany) or identified in hospitals (Spain, France, Ireland Czech Republic).
Glaser [17]
Northern California (USA)
1990–1996
19–79
310 (87)
325 (72)
Hjalgrim [12]
Denmark and Sweden
1999–2003
17–76
571 (91)
3,085 (71)
Kane [26]
North, East, and West Yorkshire, Lancashire, South Lakeland, Cornwall, South Devon, Dorset, South Hampshire (England) Italian multi-centre
1998–2004
16–67
250 (79)
234 (69)
Frequency matching by age, sex, and state of residence Twin controls Frequency matching by center, gender, and age (5-year age groups) Individual matching (Germany and Czech Republic) or frequency matching (France, Ireland, Italy, and Spain) by sex, age (5-year age groups), and residence area Frequency matching on age (5-year age groups), area, and race/ethnicity Frequency matching by country, age (10year age-groups), and sex Individual matching by sex, date of birth, and area of residence
1990–1997
19–81
360 (85)
1,763 (80)
Frequency matching by age, sex, and area Individual matching by center, sex, age, and residence Frequency matching by age, sex, and province (all males)
Miligi [16]
Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France) Alberta, Saskatchewan, Manitoba, Quebec, British Columbia, Ontario (Canada)
2000–2004
18–75
139 (97)
411 (93)
1991–1993
18–91
262 (68)
1,338 (48)
Zahm [36, 37]
Nebraska (USA) Kansas (USA)
1986–1987 1982–1984
19–98 17–94
69 (96) 116 (92)
1,384 (84) 878 (93)
Frequency matching on age (5-year age groups), race, sex, area, and vital status
Controls without a history of Hodgkin’s lymphoma, identified through random digit dialing Controls randomly selected from population registers Controls randomly selected from population registers
Controls randomly selected from Demographic or National Health Service registers Hospital controls with no history of hematological neoplasm Controls randomly selected from: Health Insurance records (Alberta Saskatchewan, Manitoba, Quebec), computerized telephone listings (Ontario), voter’s lists (British Columbia) Controls without malignancy, identified through random digit dialing, Medicare records, and mortality files
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Table 1. Characteristics of studies of Hodgkin lymphoma and cigarette smoking included in the pooled analyses
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then contribute to between-study variation in effect estimates. We anticipated some between-studies variation in effect estimates for this and other reasons, and therefore used random effects models throughout, to quantify this variation. Many of the original studies were designed and conducted but not always analyzed as individually matched studies. Data for two studies [26, 30] allowed us to obey the original individual matching. These and the other thinly stratified studies [9, 24, 33, 36, 37] were analyzed using conditional logistic regression. Further standard adjustments in the original studies, e.g. adding a linear age trend on top of adjustment for age groups were also accommodated. Most analyses followed the case–control design, but we used case-series analyses when comparing associations between HL subtypes. With respect to evaluating disease association heterogeneity, the case-series approach is preferable to conventional case–control analyses, because it circumvents biases due to different participation and recall between cases and controls [38]. In the case-series comparisons, we adjusted only for sex, age at diagnosis as a trend, and the sociodemographic confounders to retain as many informative strata as possible, under the assumption that cases had been completely sampled (irrespective of age and sex stratum). Outcomes were HL overall, histology-specific HL (nodular sclerosis or mixed cellularity), EBV-specific cHL ( positive or negative cHL), and finally the combination of the latter four. In the latter situation, adjustments were made separately for each combination of outcomes, while the effects of smoking were modeled as histology-specific and EBV status-specific. That is in the latter situation, the effect of some smoking variable z on the risk of each of the four outcomes was modeled as OR (EBV status, histology, z) = OR (histology, z) × OR (EBV status, z). Here, we call OR (histology, z) ‘the association between z and risk of histology-specific HL, taking EBV status into account’ and OR (EBV status, z) ‘the association between z and risk of
Table 2. Study characteristics and unadjusted odds ratio estimates of Hodgkin lymphoma for ever cigarette smokers versus never smokers and associated 95% confidence limits, according to substudy Data provider (substudy)
EBV status
Histology
Never smokers (cases/controls)
Ever cigarette smokers (cases/controls)
OR (95% CI)
Chang (Connecticut) Chang (Massachusetts) Cozen (USA) Dal Maso (Italy 1984–1991) Dal Maso (Italy 1998-2002) Epilymph (Spain) Epilymph (other centres) Glaser (Northern California) Hjalgrim (Denmark) Hjalgrim (Sweden) Kane (England)a Miligi (Italy) Monnereau (France) Spinelli (Canada) Zahm (Midwest) Pooled dataset
Yes Yes No No No Yes No Yes Yes Yes Yes No No No No –
Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes –
85/131 129/170 56/59 79/469 24/182 20/297 117/835 181/186 91/409 196/952 105/111 160/809 54/150 101/393 63/925 1461/6078
132/154 154/159 30/28 77/680 38/317 36/326 151/1053 129/139 147/722 137/1002 145/123 200/954 85/261 161/945 122/1337 1744/8200
1.40 (0.97–2.02) 1.34 (0.97–1.87) 1.14 (0.41–3.15) 0.74 (0.51–1.08) 1.79 (0.90–3.57) 1.29 (0.68–2.47) 1.03 (0.79–1.34) 0.99 (0.72–1.36) 1.63 (1.16–2.30) 1.08 (0.83–1.42) 1.43 (0.97–2.10) 0.97 (0.75–1.25) 0.89 (0.61–1.30) 1.10 (0.83–1.44) 1.31 (0.94–1.83) 1.13 (1.02–1.26)b
a
Not all cases were matched to a control. These excess cases only contributed to the case-series analyses. I is 6.
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number of siblings, history of infectious mononucleosis, and type of home in childhood (single-family or multiunit). Not all variables were available from all studies. For details on data collection and definitions, see references to the individual studies given in Table 1. The categorization of relative socioeconomic status was left to the contributing studies, most often based on education (see original references). HL was classified histologically according to the Revised European-American classification of Lymphoid Neoplasms and/or the 2001 World Health Organization (WHO) Classification of Hematopoietic and Lymphoid Tumors [12, 22, 24, 31], the Rye classification [16, 30, 32], or ICD for Oncology First [33, 34], Second [17, 33], or Third Editions [9, 26]. HL EBV status typing was assessed by standard immunohistochemistry for latent membrane protein (LMP)-1 and/or Epstein–Barr nuclear antigen (EBNA) and/or by in situ hybridization for EBV-encoded RNA (EBER) (Spanish EpiLymph study and [12, 17, 22, 26]). A priori it was decided to use random effects models to model variation between studies and localities. As some HL subtype outcomes were available only from a few large studies, some of these were split along state borders to allow better estimation of the random variation between studies, whereas some studies composed of methodologically similar contributions from many small study centers were aggregated to increase the likelihood of being able to provide parameter estimates from each locality. The resulting coding of study locality can be found in Table 2 under the heading ‘substudy’. We used the methodology suggested by Stukel et al. [35] to adjust the parameter estimates from each substudy for available confounders, rather than a minimal subset of globally available and semantically homogeneous confounders. All confounders were treated as covariates, except race which was coded by indicator variables for being Black, Asian, or Hispanic. Differences in confounder adjustments between studies might
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EBV status-specific HL, taking histology into account’, respectively. We aimed to correctly apportion etiologic heterogeneity in the response to smoking to the correlated outcomes positive EBV status and mixed cellularity histology. All analyses were carried out using a home-grown macro in SAS version 9.1.3 [39]. For observations analyzed by unconditional logistic regression, this macro avoided enormous or infinite estimates using techniques inspired by Clogg et al. [40] and Heinze and Schemper [41]. In the setting of multiple interest parameters, we assumed the random effects for each independent of the others. Fitting these models, we constrained each random component to have an arbitrarily selected variance of at least 0.0001. Details of the macro are available from the authors. We do not present estimates of the between-studies variation, as they were quite low. Tests for between-study variation were done by naïve likelihood-ratio tests, halving the P-value [42].
We obtained data from 12 studies delivered by 11 different providers (Table 1). In total, 3335 HL cases and 14 278 controls were included in the pooled dataset. Data were obtained mainly from frequency or individually matched studies, using population controls [12, 16, 17, 22, 26, 36, 37, 43], hospital controls [9, 31, 32], or both [24, 31]. We also included one twin study in which unaffected twins of cases were used as controls [30]. Data were separated into the 15 substudies covering substantially different geographical areas or time periods as shown in Table 2. Fourteen and seven substudies contained data on histology and EBV status, respectively. Further details can be found in the supplementary Table S1, available at Annals of Oncology online. Overall, 54.5% of cases and 57.4% of controls were ever cigarette smokers, corresponding to a slightly elevated adjusted OR of HL of 1.10 (95% CI 1.01–1.21) (Table 3). In Table 3, OR estimates of HL risk according to smoking variables, tumor EBV status, and histology are shown. Overall, compared with never cigarette smokers (OR = 1), current cigarette smoking was associated with a suggestive increased relative risk of HL overall (OR = 1.16, 95% CI 0.98–1.36), whereas the relative risk of HL overall among former cigarette smokers was not increased (OR = 1.00, 95% CI 0.84–1.21). The increased overall HL risk in current cigarette smokers reflected increased risks of mixed cellularity (OR = 1.60, 95% CI 1.29–1.99) and EBV-positive cHL (OR = 1.81, 95% CI 1.27– 2.56), and inconspicuous risks for nodular sclerosis (OR = 1.09, 95% CI 0.90–1.32) and EBV-negative cHL (OR = 1.02, 95% CI 0.72–1.44) (Table 3). The risks for mixed cellularity (OR = 1.21, 95% CI 0.95–1.54) and EBV-positive cHL (OR = 1.28, 95% CI 0.93–1.78) among former cigarette smokers compared with never cigarette smokers were elevated without being statistically significant (Table 3). Compared with never cigarette smokers, increased risks for mixed cellularity and EBV-positive cHL among current cigarette smokers were seen in both younger and older adults and among men (Table 3). Among female current cigarette smokers, the risk of EBV-positive (OR = 1.27, 95% CI 0.82– 1.96) and mixed cellularity cHL (OR = 1.23, 95% CI 0.86–1.77)
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discussion In the present analyses, self-reported history of cigarette smoking was associated with a marginally increased risk of HL overall. This is consistent with a recent large cohort study among UK women [44], and of two recent meta-analyses [27, 28] which also included case–control studies contributing to the present investigation. We also carried out detailed case–control and case-series analyses stratified according to tumor and patient characteristics separately and combined to explore the possibility of etiological heterogeneity between HL subtypes. These stratified analyses demonstrated that current cigarette smoking was associated with increased risks of mixed cellularity and EBV-positive cHL across all age groups, whereas the risks of nodular sclerosis and EBV-negative cHL were not systematically increased. These differences by tumor histology and EBV status were largely corroborated in the case-series analyses, which moreover indicated different associations with smoking when simultaneously considered. Thereby, we extend the findings of the two recent meta-analyses, which found stronger associations of smoking with risk of mixed cellularity [28] and EBV-positive HL [27]. While nuances in the association with smoking were apparent in analyses stratified by age and sex, the results suggest that, among persons with cHL, smokers and nonsmokers differ with respect to the histological phenotype and EBV status of their disease. This could imply that the smoking association may pertain predominantly or even exclusively to certain HL subtypes. Methodological issues and possible causal mechanisms must be considered to explain the observed associations. Involving data from 12 separate studies and with little evidence of statistical heterogeneity between studies, the observed associations are not likely to be chance findings. A variety of biases could have produced spurious associations consistent with the ones observed. These include participation bias, i.e.
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results
was elevated without being statistically significant in comparison to never smokers (Table 3). Compared with the case–control analyses, case-series comparisons found slightly weaker associations for the association of current cigarette smoking both with mixed cellularity versus nodular sclerosis and with EBV positivity versus EBV negativity (Table 3). Taking the correlation between EBV status and histology into account further reduced the associations, leaving current cigarette smoking positively associated only with EBV-positive cHL (Table 3). In the categorical analyses, statistically significant differences between strata were observed for all variables except years since cessation, when using never smokers as reference group (Table 4). However, when never-smokers were excluded, no statistically significant dose–response trends could be found in the analyses restricted to current smokers. Estimates of between-study variation appeared to be quite low (data not shown). For 79% (257 of 324) of the entries in Tables 3 and 4, the between-studies-variation estimate was at our a priori-defined lower limit. In 10 instances, the test for homogeneity yielded a statistically significant P-value; 10 of 324 is less than the 5% expected by chance.
Mixed cellularity versus nodular sclerosisa
EBV positive versus EBV neg.
EBV positive versus EBV neg.b
1.06 (0.89–1.26) 1.02 (0.72–1.44) 1.02 (0.79–1.33) 1 (ref.)
1.17 (0.95–1.45) 1.20 (0.95–1.52) 1.13 (0.86–1.49) 1 (ref.)
1.06 (0.79–1.42) 1.00 (0.71–1.41) 1.13 (0.78–1.62) 1 (ref.)
1.43 (1.06–1.93)A 1.63 (1.02–2.61)A 1.24 (0.86–1.79) 1 (ref.)
1.30 (0.99–1.71) 1.45 (1.02–2.05)A 1.11 (0.79–1.57) 1 (ref.)
1.85 (1.22–2.80)B 2.18 (1.42–3.36)C 1.36 (0.87–2.13) 1 (ref.)
1.09 (0.83–1.44) 1.15 (0.73–1.79) 1.01 (0.68–1.49) 1 (ref.)
1.16 (0.88–1.53) 1.20 (0.90–1.62) 1.07 (0.75–1.53) 1 (ref.)
0.95 (0.63–1.44) 0.98 (0.63–1.52) 0.92 (0.55–1.53) 1 (ref.)
1.67 (1.07–2.60)A 1.85 (1.08–3.18)A 1.27 (0.72–2.22) 1 (ref.)
1.44 (0.97–2.14) 1.51 (0.99–2.31) 1.19 (0.73–1.96) 1 (ref.)
0.99 (0.84–1.16) 1.04 (0.84–1.28) 0.97 (0.77–1.22) 1 (ref.)
1.20 (0.84–1.72) 1.27 (0.82–1.96) 1.16 (0.76–1.78) 1 (ref.)
1.04 (0.84–1.28) 1.04 (0.79–1.36) 1.08 (0.80–1.45) 1 (ref.)
1.12 (0.81–1.53) 1.09 (0.75–1.58) 1.23 (0.81–1.87) 1 (ref.)
1.03 (0.72–1.48) 0.93 (0.61–1.44) 1.15 (0.75–1.77) 1 (ref.)
1.18 (0.80–1.75) 1.21 (0.75–1.95) 1.17 (0.72–1.90) 1 (ref.)
1.08 (0.77–1.52) 1.14 (0.76–1.71) 1.03 (0.68–1.56) 1 (ref.)
1.28 (1.00–1.65) 1.33 (1.00–1.75)A 1.25 (0.89–1.77) 1 (ref.)
0.99 (0.86–1.13) 1.00 (0.82–1.22) 0.93 (0.70–1.23) 1 (ref.)
1.49 (1.08–2.05)A 1.44 (0.99–2.10) 1.49 (0.97–2.27) 1 (ref.)
0.94 (0.77–1.15) 0.87 (0.62–1.22) 0.91 (0.60–1.39) 1 (ref.)
1.23 (0.95–1.60) 1.22 (0.92–1.62) 1.32 (0.91–1.90) 1 (ref.)
1.05 (0.76–1.47) 1.00 (0.68–1.46) 1.19 (0.76–1.86) 1 (ref.)
1.67 (1.19–2.34)B 1.77 (1.17–2.67)B 1.52 (0.96–2.41) 1 (ref.)
1.41 (1.04–1.91)A 1.49 (1.05–2.11)A 1.23 (0.81–1.87) 1 (ref.)
1.43 (1.08–1.90)A 1.86 (1.34–2.59)C 1.18 (0.85–1.63) 1 (ref.)
1.23 (0.97–1.57) 1.50 (1.13–1.99)B 1.07 (0.82–1.40) 1 (ref.)
1.45 (0.95–2.21) 2.36 (1.38–4.02)B 1.11 (0.70–1.75) 1 (ref.)
1.46 (1.05–2.02)A 1.65 (1.04–2.61)A 1.34 (0.94–1.92) 1 (ref.)
1.03 (0.71–1.49) 1.07 (0.71–1.63) 0.97 (0.64–1.47) 1 (ref.)
0.93 (0.58–1.49) 0.94 (0.54–1.64) 0.92 (0.56–1.51) 1 (ref.)
1.04 (0.58–1.86) 1.50 (0.61–3.66) 0.97 (0.52–1.82) 1 (ref.)
1.01 (0.63–1.62) 1.15 (0.66–1.99) 0.93 (0.57–1.54) 1 (ref.)
1.58 (1.10–2.27)A 1.89 (1.27–2.81)B 1.43 (0.94–2.19) 1 (ref.)
1.05 (0.81–1.37) 1.07 (0.79–1.45) 1.04 (0.75–1.45) 1 (ref.)
1.34 (0.84–2.15) 1.35 (0.79–2.30) 1.18 (0.69–2.03) 1 (ref.)
0.98 (0.69–1.41) 1.05 (0.55–2.01) 1.03 (0.67–1.59) 1 (ref.)
1.14 (0.77–1.69) 1.15 (0.76–1.75) 1.12 (0.68–1.85) 1 (ref.)
1.05 (0.66–1.67) 0.98 (0.60–1.61) 1.19 (0.68–2.08) 1 (ref.)
1.31 (0.71–2.40) 1.36 (0.64–2.88) 1.19 (0.59–2.41) 1 (ref.)
1.10 (0.70–1.74) 1.13 (0.69–1.84) 1.03 (0.59–1.80) 1 (ref.)
1.26 (0.92–1.73) 1.39 (0.95–2.04) 1.06 (0.70–1.60) 1 (ref.)
1.11 (0.92–1.35) 1.17 (0.94–1.46) 1.02 (0.79–1.33) 1 (ref.)
1.43 (0.96–2.13) 1.70 (1.09–2.65)A 1.09 (0.66–1.82) 1 (ref.)
1.06 (0.80–1.40) 1.14 (0.83–1.59) 1.00 (0.70–1.43) 1 (ref.)
1.14 (0.82–1.59) 1.13 (0.79–1.62) 1.19 (0.75–1.89) 1 (ref.)
1.00 (0.68–1.47) 0.97 (0.64–1.48) 1.08 (0.64–1.82) 1 (ref.)
1.40 (0.91–2.16) 1.41 (0.88–2.27) 1.39 (0.77–2.52) 1 (ref.)
1.28 (0.89–1.86) 1.30 (0.87–1.95) 1.19 (0.72–1.97) 1 (ref.)
Nodular sclerosis
EBV positive
EBV negative
1.42 (1.17–1.72)C 1.60 (1.29–1.99)C 1.21 (0.95–1.54) 1 (ref.)
1.05 (0.94–1.18) 1.09 (0.90–1.32) 0.98 (0.84–1.14) 1 (ref.)
1.57 (1.20–2.05)B 1.81 (1.27–2.56)B 1.28 (0.93–1.78) 1 (ref.)
1.44 (1.13–1.84)B 1.68 (1.29–2.19)C 1.15 (0.86–1.55) 1 (ref.)
1.09 (0.92–1.29) 1.18 (0.93–1.50) 0.99 (0.80–1.23) 1 (ref.)
1.23 (0.91–1.65) 1.23 (0.86–1.77) 1.26 (0.87–1.82) 1 (ref.)
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Smoking status Ever smokers 1.11 (1.01–1.22)A Current smokers 1.16 (0.99–1.37) Former smokers 1.00 (0.83–1.21) Never smokers 1 (ref.) Men Ever smokers 1.17 (1.00–1.38) Current smokers 1.26 (1.02–1.56)A Former smokers 1.01 (0.80–1.26) Never smokers 1 (ref.) Women Ever smokers 1.03 (0.90–1.18) Current smokers 1.07 (0.91–1.25) Former smokers 1.00 (0.81–1.23) Never smokers 1 (ref.) Age 15–44 years Ever smokers 1.02 (0.91–1.14) Current smokers 1.00 (0.85–1.19) Former smokers 1.01 (0.77–1.31) Never smokers 1 (ref.) Age 45+ years Ever smokers 1.31 (1.11–1.55)B Current smokers 1.66 (1.37–2.01)C Former smokers 1.08 (0.89–1.31) Never smokers 1 (ref.) Socioeconomic status 1 Ever smokers 1.09 (0.89–1.34) Current smokers 1.23 (0.99–1.54) Former smokers 0.94 (0.70–1.26) Never smokers 1 (ref.) Socioeconomic status 2 Ever smokers 1.11 (0.94–1.30) Current smokers 1.16 (0.97–1.39) Former smokers 1.07 (0.81–1.42) Never smokers 1 (ref.)
Case-series Mixed cellularity versus nodular sclerosis
Mixed cellularity
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Case–control All cases
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Table 3. Adjusted odds ratio estimates for Hodgkin lymphoma overall and for EBV and histology-specific Hodgkin lymphoma subtypes and associated 95% confidence limits based on case–control and case-series modeling according to cigarette smoking status by age and sex
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b
a
The association between smoking and risk of histology-specific cHL, taking EBV status into account. The association between smoking and risk of EBV status-specific cHL, taking histology into account. *P < 0.05; **P < 0.01; ***P < 0.001.
1.13 (0.84–1.52) 1.36 (0.87–2.12) 1.11 (0.79–1.56) 1 (ref.) 1.41 (0.89–2.22) 1.64 (0.87–3.06) 1.31 (0.78–2.19) 1 (ref.) 0.98 (0.78–1.23) 1.19 (0.86–1.66) 0.91 (0.69–1.22) 1 (ref.) 1.26 (0.88–1.79) 1.29 (0.81–2.05) 1.33 (0.87–2.04) 1 (ref.) Socioeconomic status 3 Ever smokers 1.06 (0.87–1.29) Current smokers 1.11 (0.83–1.50) Former smokers 0.99 (0.77–1.28) Never smokers 1 (ref.)
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overrepresentation of individuals with healthy lifestyles among population controls, possibly balanced by the opposite, i.e. overrepresentation of less healthy individuals in studies using hospital controls, and recall bias, i.e. a tendency for cases to be more likely than controls to recall, e.g. cigarette smoking. However, a number of results argue that these biases are unlikely to be primary explanations for the observed associations. In particular, the association with cigarette smoking was observed specifically for mixed cellularity and EBV-positive cHL, whereas there was no evidence of a consistent association with the larger groups of nodular sclerosis and EBV-negative cHL. Accordingly, there are no strong reasons to believe that participation or recall should differ substantially by histology or EBV status among cases in ways that would explain this. Moreover, there also was good agreement between the case–control and case-series comparisons, the latter being less likely to suffer from the aforementioned biases [38]. There is evidence that cigarette smoking is increasingly associated with lower socioeconomic status in many populations [45]. HL histology and EBV status also vary by socioeconomic status, although the relationship is rather complex and possibly subject to secular changes [5, 46, 47]. Therefore, although all analyses were adjusted for measures of socioeconomic status residual confounding may theoretically have contributed to our observations but is unlikely to account for them entirely. The increased risk of cHL observed among current cigarette smokers, which is potentially confined to mixed cellularity and/or EBV-positive variants therefore likely reflects a causal association. Biologically, this is plausible. HL, especially the EBV-positive subtype, occurs with increased frequency upon immune incompetence [48, 49], and it is well established that tobacco smoking affects the immune system detrimentally in numerous ways [27, 50]. Altered Th1/Th2 balance, known to be associated with smoking and with HL, might be a possible causal mechanism to explain the association [30, 48]. Epidemiologically, it is also noteworthy that tobacco smoking has been associated with titers of antiEBV viral capsid antigen immunoglobulin G antibodies [51], which in turn is associated with an increased risk of EBVpositive HL [52]. In contrast to the recent meta-analyses [27, 28], we found no compelling evidence of monotonic dose–response associations between smoking and cHL risk. These conflicting study-specific findings may reflect differences in the analytical approach regarding the reference group. In the present investigation, trend estimates were assessed only within the group of current smokers, whereas, in the meta-analyses, the group of neversmokers was included in the trend estimates [27, 28]. Accordingly, when we mimicked this analytical approach, we too obtained a statistically significant trend in relative risk of all subtypes of HL combined among current smokers of similar magnitude as that of the meta-analyses [27, 28] (data not shown). However, this approach entirely disregards basic and possible smoking unrelated differences between ever and never smokers (in technical terms, the intercept in the statistical models), which was not statistically acceptable in all subanalyses (data not shown).
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1.21 (0.83–1.77) 1.21 (0.77–1.92) 1.23 (0.76–2.02) 1 (ref.)
1.04 (0.67–1.61) 1.03 (0.58–1.80) 1.03 (0.62–1.71) 1 (ref.)
1.28 (0.76–2.14) 1.66 (0.81–3.43) 1.09 (0.59–2.02) 1 (ref.)
1.09 (0.72–1.67) 1.26 (0.73–2.16) 1.00 (0.61–1.64) 1 (ref.)
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Case–control All cases
Mixed cellularity versus nodular sclerosisa
EBV positive versus EBV negative
EBV positive versus EBV negativeb
1.07 (0.65–1.75) 1.10 (0.84–1.44) 1.07 (0.69–1.66) 1 (ref.) 1.00 (0.93–1.07)
1.24 (0.84–1.85) 1.18 (0.89–1.56) 1.25 (0.74–2.12) 1 (ref.) 1.01 (0.96–1.05)
1.19 (0.73–1.92) 0.90 (0.59–1.37) 0.98 (0.47–2.04) 1 (ref.) 1.01 (0.96–1.07)
1.49 (0.91–2.45) 1.66 (1.03–2.68)A 2.22 (1.12–4.39)A 1 (ref.) 1.02 (0.96–1.07)
1.26 (0.79–1.99) 1.43 (0.95–2.17) 1.75 (0.88–3.49) 1 (ref.) 1.00 (0.95–1.06)
1.31 (0.69–2.47) 2.32 (1.49–3.60)C 2.09 (1.15–3.80)A 1 (ref.) 1.15 (0.61–2.19)
1.09 (0.71–1.68) 1.41 (1.04–1.91)A 1.47 (0.98–2.22) 1 (ref.) 0.90 (0.51–1.60)
0.90 (0.58–1.41) 1.20 (0.87–1.66) 1.37 (0.98–1.92) 1 (ref.) 1.48 (0.99–2.22)
0.67 (0.33–1.36) 0.95 (0.60–1.50) 1.07 (0.62–1.83) 1 (ref.) 1.31 (0.68–2.51)
1.66 (0.86–3.21) 1.58 (1.00–2.48) 1.28 (0.73–2.23) 1 (ref.) 1.23 (0.50–3.03)
1.52 (0.84–2.74) 1.48 (0.97–2.26) 1.17 (0.70–1.98) 1 (ref.) 0.93 (0.49–1.76)
1.06 (0.80–1.41) 0.98 (0.82–1.18) 1.45 (1.09–1.92)A 1 (ref.) 0.97 (0.80–1.18)
1.71 (0.97–3.00) 1.37 (0.89–2.11) 2.29 (1.40–3.73)B 1 (ref.) 1.05 (0.76–1.45)
1.08 (0.69–1.69) 0.89 (0.67–1.19) 1.35 (0.79–2.33) 1 (ref.) 1.07 (0.81–1.41)
1.23 (0.79–1.91) 1.21 (0.90–1.62) 1.22 (0.82–1.83) 1 (ref.) 1.06 (0.74–1.53)
1.09 (0.62–1.90) 0.87 (0.58–1.31) 1.19 (0.65–2.17) 1 (ref.) 1.03 (0.66–1.62)
1.96 (1.11–3.48)A 1.62 (1.07–2.46)A 1.38 (0.77–2.48) 1 (ref.) 0.86 (0.52–1.43)
1.42 (0.85–2.36) 1.36 (0.93–1.99) 1.40 (0.79–2.49) 1 (ref.) 0.96 (0.62–1.48)
1.15 (0.96–1.39) 1.19 (0.93–1.52) 1.21 (0.88–1.67) 1 (ref.) 0.99 (0.98–1.01)
1.47 (0.91–2.36) 2.69 (1.57–4.60)C 2.38 (1.18–4.81)A 1 (ref.) 1.01 (0.99–1.03)
1.22 (0.90–1.65) 1.45 (0.98–2.15) 1.43 (0.72–2.85) 1 (ref.) 1.00 (0.98–1.02)
1.04 (0.76–1.42) 1.33 (0.91–1.94) 1.34 (0.87–2.08) 1 (ref.) 1.01 (0.99–1.02)
0.72 (0.45–1.14) 1.31 (0.73–2.35) 1.06 (0.50–2.26) 1 (ref.) 1.01 (0.98–1.03)
1.70 (0.95–3.01) 1.21 (0.67–2.21) 1.58 (0.67–2.72) 1 (ref.) 1.01 (0.97–1.04)
1.52 (1.00–2.30) 1.14 (0.64–2.00) 1.47 (0.70–3.11) 1 (ref.) 1.00 (0.97–1.02)
Nodular sclerosis EBV positive
EBV negative
1.82 (1.26–2.62)B 1.64 (1.26–2.14)C 1.34 (0.90–2.00) 1 (ref.) 0.97 (0.91–1.03)
1.04 (0.82–1.31) 1.16 (0.97–1.39) 1.03 (0.78–1.37) 1 (ref.) 0.98 (0.94–1.03)
1.89 (1.18–3.01)B 1.66 (1.08–2.54)A 2.35 (1.38–4.00)B 1 (ref.) 0.96 (0.88–1.05)
1.17 (0.75–1.82) 1.80 (1.32–2.46)C 1.68 (1.18–2.39)B 1 (ref.) 1.33 (0.94–1.89)
1.25 (0.98–1.59) 1.15 (0.93–1.41) 1.11 (0.86–1.42) 1 (ref.) 0.82 (0.59–1.15)
1.08 (0.68–1.71) 1.43 (1.04–1.97)A 2.17 (1.52–3.09)C 1 (ref.) 1.24 (0.97–1.59)
1.11 (0.81–1.53) 2.00 (1.27–3.15)B 2.34 (1.60–3.42)C 1 (ref.) 1.01 (0.99–1.02)
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Initiation age (years)c ≤14 1.08 (0.84–1.38) 15–19 1.24 (1.09–1.42)B ≥20 1.00 (0.80–1.26) Never smokers 1 (ref.) ‘Trend per 0.99 (0.97–1.01) year’ Cigarettes per dayc 1–9 1.07 (0.89–1.30) 10–19 1.25 (1.04–1.51)A ≥20 1.26 (1.08–1.48)B Never smokers 1 (ref.) ‘Trend per 20 1.03 (0.85–1.24) cigarettes’ Smoking duration (years)c 0–9 0.96 (0.75–1.23) 10–29 1.06 (0.92–1.22) ≥30 1.68 (1.39–2.02)C Never smokers 1 (ref.) ‘Trend per10 1.15 (1.00–1.33) years’ Pack yearsc ≤14 1.05 (0.91–1.22) 15–29 1.37 (1.09–1.73)B ≥30 1.65 (1.34–2.03)C Never smokers 1 (ref.) ‘Trend per 1.01 (1.00–1.01) year’
Case series Mixed cellularity versus nodular sclerosis
Mixed cellularity
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| Kamper-Jørgensen et al.
Table 4. Adjusted odds ratio estimates of Hodgkin lymphoma and associated 95% confidence limits according to smoking status, EBV status, and histology, stratified on age and sex. Based on case–control and case-series modeling, respectively
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b
a
The association between smoking and risk of histology-specific cHL, taking EBV status into account. The association between smoking and risk of EBV status-specific cHL, taking histology into account. c Among current smokers. d Among former smokers. *P < 0.05; **P < 0.01; ***P < 0.001.
1.28 (0.79–2.09) 0.72 (0.37–1.41) 1.19 (0.77–1.83) 1 (ref.) 1.05 (0.80–1.38) 0.79 (0.46–1.34) 0.81 (0.38–1.73) 0.95 (0.67–1.33) 1 (ref.) 1.14 (0.93–1.40) 1.11 (0.50–2.46) 1.24 (0.59–2.59) 1.09 (0.65–1.83) 1 (ref.) 1.12 (0.87–1.44) 1.08 (0.80–1.47) 0.84 (0.60–1.18) 0.85 (0.65–1.09) 1 (ref.) 0.86 (0.72–1.03) 1.77 (1.12–2.81)A 0.87 (0.48–1.57) 0.98 (0.68–1.42) 1 (ref.) 0.92 (0.75–1.13) Years since cessationd 0–4 1.22 (0.98–1.52) 5–9 0.89 (0.69–1.14) ≥10 0.83 (0.68–1.01) Never smokers 1 (ref.) ‘Trend per 10 0.84 (0.71–1.00)A years’
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The absence of an effect of age at cigarette-smoking initiation, pack-years, and duration of cigarette smoking in the present study could also be explained if smoking only influenced one or more late stages in lymphomagenesis [20]. In the extreme, this would imply that only the cigarette-smoking pattern immediately before symptomatic cancer development would influence the risk pattern. The age-specific incidence pattern for HL, with its large fraction of cases in adolescents and younger adults, and evidence that the interval from EBV primary infection to diagnosis of EBV-positive HL may be on the order of a few years [53, 54], make it plausible that recent exposures could be particularly important to risk. This mechanism would explain the absence of dose–response associations between HL risk and age at initiation of cigarette smoking, duration of cigarette smoking, and cumulative cigarette-smoking exposure, and would be consistent with former cigarette smoking being a less strong risk factor for HL than current cigarette smoking. Technically, the inconspicuous trend in HL risk with increasing cigarette smoking intensity could also be due to inadequate statistical power alone or in combination with inhomogeneous exposure information in the contributing studies. Accordingly, despite the large number of cases included, only 197 current cigarette smokers with mixed cellularity HL and 108 current cigarette smokers with EBV-positive HL were available for the dose–response subanalyses. Stratified analyses showed that smoking history differed between different strata of patients and HL subtypes. The association was stronger in men than in women, stronger in older than in younger adults, stronger for mixed cellularity than for nodular sclerosis cHL, and stronger for EBV-positive than for EBV-negative cHL. The latter variation by EBV status potentially could explain the other differences. Accordingly, the proportion of EBV-positive cases is consistently higher in men than in women, higher in older than in younger adults, and higher in mixed cellularity cHL than in NS cHL [6]. To disentangle the different associations, we carried out case-series analyses that included both cHL histological subtype and EBV status. Albeit with limited statistical precision, these analyses indicated that the association with current cigarette smoking was governed more by HL EBV status than by HL histology. This adds further to the two recent meta-analyses [27, 28], which did not conduct analyses stratified by both histology and EBV status. However, not all observations were consistent with a restriction of association to EBV-positive HL. Specifically, increased risks were seen across all subtypes of cHL in currently smoking older adults, and accordingly, in this age group, no differences between cHL variants were seen in the case-series analyses. Moreover, though elevated risk estimates were observed for both mixed cellularity and EBV-positive cHL in women, neither of these was formally statistically significant. It therefore remains to be determined whether these findings represent chance variations and/or age and sex-dependent variation in the effect of smoking on HL risk such that, in etiological investigations, HL should be stratified by combining sex, age, histology, and viral status. Among the strengths of our investigation is the large number of cases and controls, allowing for analyses stratified by HL subtype as well as both case–control and case-series
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1.14 (0.46–2.80) 0.71 (0.27–1.84) 1.21 (0.69–2.13) 1 (ref.) 0.99 (0.72–1.34)
1.92 (0.81–4.52) 1.22 (0.53–2.81) 1.38 (0.77–2.46) 1 (ref.) 1.04 (0.70–1.57)
1.55 (0.69–3.48) 0.82 (0.35–1.94) 1.31 (0.75–2.29) 1 (ref.) 1.08 (0.79–1.46)
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acknowlegements The authors thank the following funders for contribution to individual studies: NIH, Nordic Cancer Union, Plan Danmark, and the Italian Association for Cancer Research.
funding The study was supported by NIH (grant number 5 ROI CA 69269), Nordic Cancer Union (no grant number), Plan
| Kamper-Jørgensen et al.
Danmark grant number is 16-02-D, and the Italian Association for Cancer Research (no grant number).
disclosure The authors have declared no conflicts of interest.
references 1. Mueller N, Grufferman S, Schottenfeld D et al Hodgkin lymphoma Cancer Epidemiology and Prevention. New York, USA: Oxford University Press, 2006. 2. Curado MP, Edwards B, Shin HR et al (eds). Cancer Incidence Five Continents, Vol. IX. Lyon, France: International Agency for Research on Cancer 2007; 160. 3. MacMahon B. Epidemiology of Hodgkin’s disease. Cancer Res 1966; 26: 1189–201. 4. MacMahon B. Epidemiological evidence of the nature of Hodgkin’s disease. Cancer 1957; 10: 1045–54. 5. Cozen W, Katz J, Mack TM. Risk patterns of Hodgkin’s disease in Los Angeles vary by cell type. Cancer Epidemiol Biomarkers Prev 1992; 1: 261–8. 6. Glaser SL, Lin RJ, Stewart SL et al Epstein-Barr virus-associated Hodgkin’s disease: epidemiologic characteristics in international data. Int J Cancer 1997; 70: 375–82. 7. Bernard SM, Cartwright RA, Darwin CM et al Hodgkin’s disease: case control epidemiological study in Yorkshire. Br J Cancer 1987; 55: 85–90. 8. Newell GR, Rawlings W, Kinnear BK et al Case-control study of Hodgkin’s disease. I. Results of the interview questionnaire. J Natl Cancer Inst 1973; 51: 1437–41. 9. Monnereau A, Orsi L, Troussard X et al Cigarette smoking, alcohol drinking, and risk of lymphoid neoplasms: results of a French case-control study. Cancer Causes Control 2008; 19: 1147–60. 10. Williams RR, Horm JW. Association of cancer sites with tobacco and alcohol consumption and socioeconomic status of patients: interview study from the Third National Cancer Survey. J Natl Cancer Inst 1977; 58: 525–47. 11. Abramson JH, Pridan H, Sacks MI et al A case-control study of Hodgkin’s disease in Israel. J Natl Cancer Inst 1978; 61: 307–14. 12. Hjalgrim H, Ekstrom-Smedby K, Rostgaard K et al Cigarette smoking and risk of Hodgkin lymphoma: a population-based case-control study. Cancer Epidemiol Biomarkers Prev 2007; 16: 1561–6. 13. Fernberg P, Odenbro A, Bellocco R et al Tobacco use, body mass index and the risk of malignant lymphomas—a nationwide cohort study in Sweden. Int J Cancer 2006; 118: 2298–302. 14. Matthews ML, Dougan LE, Thomas DC et al Interpersonal linkage among Hodgkin’s disease patients and controls in Western Australia. Cancer 1984; 54: 2571–9. 15. Siemiatycki J, Krewski D, Franco E et al Associations between cigarette smoking and each of 21 types of cancer: a multi-site case-control study. Int J Epidemiol 1995; 24: 504–14. 16. Stagnaro E, Ramazzotti V, Crosignani P et al Smoking and hematolymphopoietic malignancies. Cancer Causes Control 2001; 12: 325–34. 17. Glaser SL, Keegan TH, Clarke CA et al Smoking and Hodgkin lymphoma risk in women United States. Cancer Causes Control 2004; 15: 387–97. 18. Hammond EC, Horn D. Smoking and death rates; report on forty-four months of follow-up of 187,783 men. II. Death rates by cause. J Am Med Assoc 1958; 166: 1294–308. 19. Paffenbarger RS, Jr, Wing AL, Hyde RT. Characteristics in youth predictive of adult-onset malignant lymphomas, melanomas, and leukemias: brief communication. J Natl Cancer Inst 1978; 60: 89–92. 20. Briggs NC, Hall HI, Brann EA et al Cigarette smoking and risk of Hodgkin’s disease: a population-based case-control study. Am J Epidemiol 2002; 156: 1011–20. 21. McLaughlin JK, Hrubec Z, Blot WJ et al Smoking and cancer mortality among U.S. veterans: a 26-year follow-up. Int J Cancer 1995; 60: 190–3. 22. Chang ET, Zheng T, Weir EG et al Childhood social environment and Hodgkin’s lymphoma: new findings from a population-based case-control study. Cancer Epidemiol Biomarkers Prev 2004; 13: 1361–70.
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comparisons. In addition, all studies except one [30] included patients with incident HL, and all cases were classified according to standard criteria. Misclassification may nevertheless have occurred between histological subtypes of HL, most likely so that cases of nodular sclerosis cHL were classified as another HL subtype [55, 56]. However, such misclassification cannot explain the association observed with history of cigarette smoking for HL overall, nor for mixed cellularity and EBVpositive cHL. Another limitation lies in the use of self-reported smoking data. Across studies, history of cigarette smoking was assessed using identically structured questionnaires in cases and controls, and self-reported smoking status has previously been found to be reasonably reliable [57] and valid [58]. However, the covariates available from the included studies may not have been optimal to assess the etiologic association with HL risk. In particular, smoking intensity, derived as an average over a smoking career may be quite different from current or recent smoking intensity, which may be the causally more relevant exposure. Most likely, this misclassification would have driven the observed association with smoking intensity toward the null. Meta-regression of study characteristics on the outcomes from each substudy for some of the most frequent HL outcomes suggested that traditional study characteristics, such as types of controls and prevalence of exposure in controls, were of little importance for the presented results. However, this analysis also suggested some heterogeneity in the way ever cigarette smoking HL cases were classified into former and current smokers, and that this classification occasionally could not avoid reverse causality. Thus, for some American substudies, we found current smoking to be protective, while former smoking increased HL risk. For most European substudies, former smoking seemed protective, which was also counterintuitive (data not shown). Very detailed information about the timing of case-definition, interview, exposure cessation, etc., or prospective studies seems to be the way to avoid this problem in future studies. To summarize, we observed a positive association between current smoking and risk of cHL, which applied to all subtypes as defined by histology, age, and EBV status, respectively, with the exceptions of nodular sclerosis and EBV-negative cHL in younger adults, respectively. This finding identifies tobacco smoking to be a strong candidate as a modifiable risk factor for a subset of cHL. Importantly, our analyses also support previous speculations of etiological heterogeneity in cHL, which emphasizes the need to take subtype markers into consideration in future investigations.
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43. Pahwa P, Karunanayake CP, Spinelli JJ et al Ethnicity and incidence of Hodgkin lymphoma in Canadian population. BMC Cancer 2009; 9: 141. 44. Kroll ME, Murphy F, Pirie K et al Alcohol drinking, tobacco smoking and subtypes of haematological malignancy in the UK Million Women Study. Br J Cancer 2012; 107: 879–887. 45. WHO Regional Office for Europe. The European Tobacco Control Report 2007. Copenhagen, Denmark: World Health Organization, 2007. 46. Clarke CA, Glaser SL, Keegan TH et al Neighborhood socioeconomic status and Hodgkin’s lymphoma incidence in California. Cancer Epidemiol Biomarkers Prev 2005; 14: 1441–1447. 47. Glaser SL, Gulley ML, Clarke CA et al Racial/ethnic variation in EBV-positive classical Hodgkin lymphoma in California populations. Int J Cancer 2008; 123: 1499–1507. 48. Mani H, Jaffe ES. Hodgkin lymphoma: an update on its biology with new insights into classification. Clin Lymphoma Myeloma 2009; 9: 206–216. 49. Gandhi MK, Tellam JT, Khanna R. Epstein-Barr virus-associated Hodgkin’s lymphoma. Br J Haematol 2004; 125: 267–281. 50. Sopori M. Effects of cigarette smoke on the immune system. Nat Rev Immunol 2002; 2: 372–377. 51. Nielsen TR, Pedersen M, Rostgaard K et al Correlations between Epstein-Barr virus antibody levels and risk factors for multiple sclerosis in healthy individuals. Mult Scler 2007; 13: 420–423. 52. Levin LI, Chang ET, Ambinder RF et al Atypical prediagnosis Epstein-Barr virus serology restricted to EBV-positive Hodgkin lymphoma. Blood 2012; 120: 3750–3755. 53. Hjalgrim H, Smedby KE, Rostgaard K et al Infectious mononucleosis, childhood social environment, and risk of Hodgkin lymphoma. Cancer Res 2007; 67: 2382–2388. 54. Hjalgrim H, Askling J, Rostgaard K et al Characteristics of Hodgkin’s lymphoma after infectious mononucleosis. N Engl J Med 2003; 349: 1324–1332. 55. Glaser SL, Dorfman RF, Clarke CA. Expert review of the diagnosis and histologic classification of Hodgkin disease in a population-based cancer registry: interobserver reliability and impact on incidence and survival rates. Cancer 2001; 92: 218–224. 56. Jarrett RF, Krajewski AS, Angus B et al The Scotland and Newcastle epidemiological study of Hodgkin’s disease: impact of histopathological review and EBV status on incidence estimates. J Clin Pathol 2003; 56: 811–816. 57. D’Avanzo B, La VC, Katsouyanni K et al Reliability of information on cigarette smoking and beverage consumption provided by hospital controls. Epidemiology 1996; 7: 312–315. 58. Patrick DL, Cheadle A, Thompson DC et al The validity of self-reported smoking: a review and meta-analysis. Am J Public Health 1994; 84: 1086–1093. 59. Mack TM, Cozen W, Shibata DK et al Concordance for Hodgkin’s disease in identical twins suggesting genetic susceptibility to the young-adult form of the disease. N Engl J Med 1995; 332: 413–418. 60. Tavani A, La VC, Franceschi S et al Medical history and risk of Hodgkin’s and nonHodgkin’s lymphomas. Eur J Cancer Prev 2000; 9: 59–64. 61. Karunanayake CP, Spinelli JJ, McLaughlin JR et al Hodgkin lymphoma and pesticides exposure in men: a Canadian case-control study. J Agromed 2012; 17: 30–39.
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23. Nieters A, Rohrmann S, Becker N et al Smoking and lymphoma risk in the European prospective investigation into cancer and nutrition. Am J Epidemiol 2008; 167: 1081–9. 24. Besson H, Brennan P, Becker N et al Tobacco smoking, alcohol drinking and Hodgkin’s lymphoma: a European multi-centre case-control study (EPILYMPH). Br J Cancer 2006; 95: 378–84. 25. Nieters A, Deeg E, Becker N. Tobacco and alcohol consumption and risk of lymphoma: results of a population-based case-control study in Germany. Int J Cancer 2006; 118: 422–30. 26. Willett EV, O’Connor S, Smith AG et al Does smoking or alcohol modify the risk of Epstein-Barr virus-positive or -negative Hodgkin lymphoma? Epidemiology 2007; 18: 130–6. 27. Castillo JJ, Dalia S, Shum H. Meta-analysis of the association between cigarette smoking and incidence of Hodgkin’s Lymphoma. J Clin Oncol 2011; 29: 3900–6. 28. Sergentanis TN, Kanavidis P, Michelakos T et al Cigarette smoking and risk of lymphoma in adults: a comprehensive meta-analysis on Hodgkin and non-Hodgkin disease. Eur J Cancer Prev 2013; 22: 131–50. 29. Boffetta P, Armstrong B, Linet M et al Consortia in cancer epidemiology: lessons from InterLymph. Cancer Epidemiol Biomarkers Prev 2007; 16: 197–9. 30. Cozen W, Hamilton AS, Zhao P et al A protective role for early oral exposures in the etiology of young adult Hodgkin lymphoma. Blood 2009; 114: 4014–20. 31. Talamini R, Montella M, Crovatto M et al Non-Hodgkin’s lymphoma and hepatitis C virus: a case–control study from northern and southern Italy. Int J Cancer 2004; 110: 380–5. 32. Gallus S, Giordano L, Altieri A et al Cigarette smoking and risk of Hodgkin’s disease. Eur J Cancer Prev 2004; 13: 143–4. 33. Karunanayake CP, Singh GV, Spinelli JJ et al Occupational exposures and Hodgkin Lymphoma: Canadian case-control study. J Occup Environ Med 2009; 51: 1447–54. 34. Zahm SH, Blair A, Holmes FF et al A case-referent study of soft-tissue sarcoma and Hodgkin’s disease. Farming and insecticide use. Scand J Work Environ Health 1988; 14: 224–30. 35. Stukel TA, Demidenko E, Dykes J et al Two-stage methods for the analysis of pooled data. Stat Med 2001; 20: 2115–30. 36. Hoar SK, Blair A, Holmes FF et al Agricultural herbicide use and risk of lymphoma and soft-tissue sarcoma. JAMA 1986; 256: 1141–7. 37. Zahm SH, Weisenburger DD, Holmes FF et al Tobacco and non-Hodgkin’s lymphoma: combined analysis of three case-control studies (United States). Cancer Causes Control 1997; 8: 159–66. 38. Begg CB, Zhang ZF. Statistical analysis of molecular epidemiology studies employing case-series. Cancer Epidemiol Biomarkers Prev 1994; 3: 173–5. 39. Documentation for SAS products and solutions. http://support.sas.com/ documentation/onlinedoc/index.html. 2010. Electronic Citation. 40. Clogg CC. Multiple imputation of industry and occupational codes in census public-use samples using Bayesian logistic regression. J Am Stat Assoc 1991; 86: 68–78. 41. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med 2002; 21: 2409–2419. 42. van Houwelingen HC, Arends LR, Stijnen T. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med 2002; 21: 589–624.
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reviews Annals of Oncology 24: 2245–2255, 2013 doi:10.1093/annonc/mdt218 Published online 19 June 2013
Cigarette smoking and risk of Hodgkin lymphoma and its subtypes: a pooled analysis from the International Lymphoma Epidemiology Consortium (InterLymph) M. Kamper-Jørgensen1,2*, K. Rostgaard2, S. L. Glaser3,9, S. H. Zahm4, W. Cozen5, K. E. Smedby6, S. Sanjosé7, E. T. Chang8,9, T. Zheng25, C. La Vecchia11,12, D. Serraino13, A. Monnereau14,15, E. V. Kane16, L. Miligi17, P. Vineis18, J. J. Spinelli19,20, J. R. McLaughlin21,22, P. Pahwa23,24, J. A. Dosman23, M. Vornanen33, L. Foretova10, M. Maynadie29, A. Staines, N. Becker26, A. Nieters28, P. Brennan30, P. Boffetta31,32, P. Cocco27 & H. Hjalgrim2 1
Department of Public Health, University of Copenhagen, Copenhagen; 2Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark; Department of Research, Cancer Prevention Institute of California, Fremont; 4Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville; 5 Department of Preventive Medicine and Pathology, University of Southern California, Los Angeles, USA; 6Unit of Clinical Epidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; 7Unit of Infections and Cancer, Institut Catalá d’Oncologia, Barcelona, Spain; 8Health Sciences Practice, Exponent, Inc., Menlo Park; 9 Department of Health Research and Policy, Stanford University School of Medicine, Stanford, USA; 10Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic; 11Department of Epidemiology, Istituto di Ricerche Farmacologiche ‘Mario Negri’ IRCCS; 12Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan; 13Epidemiology and Biostatistics Unit, Scientific Directorate, IRCCS Centro Riferimento Oncologico, Aviano, Italy; 14Environmental Epidemiology of Cancer Group, Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018 Villejuif; 15 Registry of Hematological Malignancies in Gironde, Department of Clinical Research and Medical Information, Bergonié Institute, Bordeaux, France; 16Department of Health Sciences, University of York, Yorkshire, UK; 17Environmental and Occupational Epidemiology Unit, ISPO-Cancer Prevention and Research Institute, Florence, Italy; 18 Environmental Epidemiology, Imperial College London, London, UK; 19BC Cancer Agency, Cancer Control Research, Vancouver; 20School of Population and Public Health, University of British Columbia, Vancouver; 21Dalla Lana School of Public Health, University of Toronto, Toronto; 22Samuel Lunenfeld Research Institute, Mount Sinai Hospital Joseph and Wolf Lebovic Health Complex, Toronto; 23Canadian Centre for Health and Safety in Agriculture; 24Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada; 25Department of Epidemiology and Public Health, Yale University, New Haven, USA; 26Department of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; 27Department of Public Health, University of Cagliari, Monserrato, Italy; 28Department of Cancer Epidemiology and Genetics, Universitätsklinikum Freiburg, Germany; 29Registre des Hémopathies Malignes de Côte d’Or (EA4184), University of Burgundy and University Hospital, Dijon; 30Genetics Section, International Agency for Research on Cancer, Lyon, France; 31The Tisch Cancer Institute and Institute for Translational Epidemiology, Mount Sinai School of Medicine, New York, USA; 32International Prevention Research Institute, Lyon, France; 33Department of Pathology, Fimlab Laboratories, Tampere, Finland 3
Background: The etiology of Hodgkin lymphoma (HL) remains incompletely characterized. Studies of the association between smoking and HL have yielded ambiguous results, possibly due to differences between HL subtypes. Patients and methods: Through the InterLymph Consortium, 12 case–control studies regarding cigarette smoking and HL were identified. Pooled analyses on the association between smoking and HL stratified by tumor histology and Epstein–Barr virus (EBV) status were conducted using random effects models adjusted for confounders. Analyses included 3335 HL cases and 14 278 controls. Results: Overall, 54.5% of cases and 57.4% of controls were ever cigarette smokers. Compared with never smokers, ever smokers had an odds ratio (OR) of HL of 1.10 [95% confidence interval (CI) 1.01–1.21]. This increased risk reflected associations with mixed cellularity cHL (OR = 1.60, 95% CI 1.29–1.99) and EBV-positive cHL (OR = 1.81, 95% CI 1.27– 2.56) among current smokers, whereas risk of nodular sclerosis (OR = 1.09, 95% CI 0.90–1.32) and EBV-negative HL (OR = 1.02, 95% CI 0.72–1.44) was not increased. Conclusion: These results support the notion of etiologic heterogeneity between HL subtypes, highlighting the need for HL stratification in future studies. Even if not relevant to all subtypes, our study emphasizes that cigarette smoking should be added to the few modifiable HL risk factors identified. Key words: Hodgkin lymphoma, case–control, cigarette smoking, epidemiology, Epstein–Barr virus, individual patient data meta-analysis
*Correspondence to: Dr Mads Kamper-Jørgensen, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, Postbox 2099, DK-1014 Copenhagen K, Denmark, Tel: +45-35327391; Fax: +45-35351181; E-mail [email protected]
© The Author 2013. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected].
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Received 15 December 2012; revised 26 April 2013; accepted 2 May 2013
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introduction
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countries, epidemiological investigations have often suffered from limited statistical power to detect risk factors for HL overall, and even more so for HL subtypes. To address this limitation, in the present investigation, we took advantage of an international collaboration across 12 case–control studies totaling more than 3300 patients with HL and more than 14,000 controls without HL with individual level exposure data, information about HL histology and, in a subset, HL EBV status.
materials and methods Candidate studies were identified through the InterLymph Consortium [29]. Original case–control studies with electronically available data and information regarding cigarette smoking and HL were included in the study pool. From studies with relevant data, we also requested information on tumor histology and EBV status. Smoking history was assessed using the following variables: ever versus never tobacco smoking, type of tobacco smoking (cigarettes, other, or both); and for cigarette smokers, current or former smoking status, age at initiation, average number of cigarettes smoked per day, duration of smoking, and time since cessation. In an included study of twins [30], the interview data were obtained many years after HL diagnosis. The smoking variables, age, and time were transformed to reflect the situation at the time of diagnosis of the case twin. Smoking variables from individual studies were harmonized to fit common definitions of ‘ever cigarette smoker’, ‘current cigarette smoker’, and ‘former cigarette smoker’. These common definitions aimed at establishing the smoking status at the time of diagnosis in cases, rather than at the time of interview. Former cigarette smoking implied cessation for such a long time (≥1 year) that it was not primarily a matter of reverse causation. However, two American studies had information on time since cessation only for a small fraction of former smokers, making this correction ineffective in these cases. Forty-nine cases and 30 controls were recoded from former to current smokers to fulfill this requirement. We defined participants as ever cigarette smokers based on the criteria available in each study. Inclusion in this study required the participant to either have a cigarettesmoking history or to have never-smoked tobacco. The duration of cigarette smoking was defined by the individual studies, and pack-years were calculated as the product of the duration of cigarette smoking and cigarette-smoking intensity. Categorical analyses for initiation age, cigarettes per day, smoking duration, and pack years were carried out on current compared with never cigarette smokers. Dose–response analyses regarding years since cessation were carried out on the group of former compared with never cigarette smokers. Dose–response trend analyses were restricted to either current or former cigarette smokers as appropriate. Besides HL and cigarette-smoking-related data, we requested interview data on age at diagnosis (or pseudo-diagnosis for controls), sex, calendar year, locality, race (White, Black, Asian, other), current and childhood socioeconomic status (each coded as 3: low, 2: mid, 1: high), pre-school childcare attendance,
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Despite considerable efforts, the etiology of classical Hodgkin lymphoma (cHL) remains incompletely characterized with few risk factors firmly established [1]. Because cHL is among the most common malignancies among adolescents and younger adults in industrialized countries in the Western hemisphere [2], and because high cure rates have hitherto been accompanied by considerable treatment-related morbidity among survivors, research on risk factors is warranted. The uncertainty regarding risk factors may reflect that cHL comprises a number of etiologically diverse entities [3, 4], which have been difficult to define precisely. Accordingly, cHL can be grouped according to age at diagnosis (children, younger adults, older adults), histological subtype (nodular sclerosis, mixed cellularity, lymphocyte rich, lymphocyte depleted), and the presence/absence of Epstein–Barr virus (EBV) in the malignant Hodgkin/Reed-Sternberg cells [3–6]. While these criteria are correlated, e.g. the predominant type of cHL in younger adults is nodular sclerosis, the majority of which is EBV negative, they are at the same time too incongruent to be mutually interchangeable. Therefore, the interpretation and comparison of epidemiological studies employing different stratifications of cHL and including different proportions of each subtype in the patient population can be difficult. Thus, studies are warranted which may lead to the definition of etiologically distinct cHL subtypes, e.g. by identifying subtype-specific risk factors. Tobacco smoking is a potential cHL risk factor worth scrutinizing. Though the number of studies is relatively limited, findings have been inconsistent, e.g. both statistically significantly reduced [7], statistically unchanged [8–19] and statistically significantly increased [20–26] risks of HL overall have been reported among ever smokers. The inconsistent findings may reflect various methodological issues, such as limited statistical power because of small sample sizes, biases related to choice of comparison groups, study participation, exposure assessment, and uncontrolled confounding. However, it may also reflect uneven distributions of cHL variants between the studies, as described above. For example Briggs et al. observed a 1.3-fold significantly increased risk of HL overall among ever versus never smokers [20]. The increased HL risk appeared to be restricted to current smokers, among whom the association was considerably stronger for mixed cellularity HL [odds ratio (OR) = 3.4, 95% confidence interval (CI) 1.8–6.4] than for the nodular sclerosis HL variant (OR = 1.6, 95% CI 1.1–2.3). Two recent meta-analyses reached the conclusion that cigarette smoking increases the overall risk of HL [27, 28]. One meta-analysis of 14 case–control studies and 3 cohort studies reported a stronger association of current smoking with risk of EBV-positive HL (summary relative risk [RR] = 2.26) than EBV-negative HL (summary RR = 1.40) [27]. The other metaanalysis, which included 17 case–control studies and 4 cohort studies of HL, reported a stronger association of current smoking with risk of mixed cellularity HL (summary RR = 2.53) than nodular sclerosis HL (summary RR = 1.35) [28]. Neither of the two, however, simultaneously considered tumor histology and EBV status. Because HL is a relatively rare disease with annual incidence rates around 2–4 per 100 000 person-years in Western
Annals of Oncology
Data provider (reference)
Location
Period of interview
Age at (pseudo) diagnosis
Cases (% part)
Controls (% part)
Matching
Source of controls
Chang [22]
Massachusetts, Connecticut (USA)
1998–2002
15–81
500 (85)
614 (56)
Cozen [30, 59] Dal Maso [31, 60] EpiLymph [24]
USA and Canada Aviano, Milan (Italy) Aviano, Naples (Italy) Spain, France, Italy, Germany, Ireland, Finland and Czech Republic
1961–1992 1983–1992 1998–2002 1998–2004
14–49 14–85 18–83 16–100
86 (70) 156 (97) 62 (97) 324 (88)
87 (70) 1,149 (97) 499 (91) 2,511 (81)
Population controls without a prior history of Hodgkin’s lymphoma Twin controls Hospital controls admitted for acute, nonneoplastic, nonimmunological conditions Controls without cancer drawn from population registers (Italy and Germany) or identified in hospitals (Spain, France, Ireland Czech Republic).
Glaser [17]
Northern California (USA)
1990–1996
19–79
310 (87)
325 (72)
Hjalgrim [12]
Denmark and Sweden
1999–2003
17–76
571 (91)
3,085 (71)
Kane [26]
North, East, and West Yorkshire, Lancashire, South Lakeland, Cornwall, South Devon, Dorset, South Hampshire (England) Italian multi-centre
1998–2004
16–67
250 (79)
234 (69)
Frequency matching by age, sex, and state of residence Twin controls Frequency matching by center, gender, and age (5-year age groups) Individual matching (Germany and Czech Republic) or frequency matching (France, Ireland, Italy, and Spain) by sex, age (5-year age groups), and residence area Frequency matching on age (5-year age groups), area, and race/ethnicity Frequency matching by country, age (10year age-groups), and sex Individual matching by sex, date of birth, and area of residence
1990–1997
19–81
360 (85)
1,763 (80)
Frequency matching by age, sex, and area Individual matching by center, sex, age, and residence Frequency matching by age, sex, and province (all males)
Miligi [16]
Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France) Alberta, Saskatchewan, Manitoba, Quebec, British Columbia, Ontario (Canada)
2000–2004
18–75
139 (97)
411 (93)
1991–1993
18–91
262 (68)
1,338 (48)
Zahm [36, 37]
Nebraska (USA) Kansas (USA)
1986–1987 1982–1984
19–98 17–94
69 (96) 116 (92)
1,384 (84) 878 (93)
Frequency matching on age (5-year age groups), race, sex, area, and vital status
Controls without a history of Hodgkin’s lymphoma, identified through random digit dialing Controls randomly selected from population registers Controls randomly selected from population registers
Controls randomly selected from Demographic or National Health Service registers Hospital controls with no history of hematological neoplasm Controls randomly selected from: Health Insurance records (Alberta Saskatchewan, Manitoba, Quebec), computerized telephone listings (Ontario), voter’s lists (British Columbia) Controls without malignancy, identified through random digit dialing, Medicare records, and mortality files
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Table 1. Characteristics of studies of Hodgkin lymphoma and cigarette smoking included in the pooled analyses
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then contribute to between-study variation in effect estimates. We anticipated some between-studies variation in effect estimates for this and other reasons, and therefore used random effects models throughout, to quantify this variation. Many of the original studies were designed and conducted but not always analyzed as individually matched studies. Data for two studies [26, 30] allowed us to obey the original individual matching. These and the other thinly stratified studies [9, 24, 33, 36, 37] were analyzed using conditional logistic regression. Further standard adjustments in the original studies, e.g. adding a linear age trend on top of adjustment for age groups were also accommodated. Most analyses followed the case–control design, but we used case-series analyses when comparing associations between HL subtypes. With respect to evaluating disease association heterogeneity, the case-series approach is preferable to conventional case–control analyses, because it circumvents biases due to different participation and recall between cases and controls [38]. In the case-series comparisons, we adjusted only for sex, age at diagnosis as a trend, and the sociodemographic confounders to retain as many informative strata as possible, under the assumption that cases had been completely sampled (irrespective of age and sex stratum). Outcomes were HL overall, histology-specific HL (nodular sclerosis or mixed cellularity), EBV-specific cHL ( positive or negative cHL), and finally the combination of the latter four. In the latter situation, adjustments were made separately for each combination of outcomes, while the effects of smoking were modeled as histology-specific and EBV status-specific. That is in the latter situation, the effect of some smoking variable z on the risk of each of the four outcomes was modeled as OR (EBV status, histology, z) = OR (histology, z) × OR (EBV status, z). Here, we call OR (histology, z) ‘the association between z and risk of histology-specific HL, taking EBV status into account’ and OR (EBV status, z) ‘the association between z and risk of
Table 2. Study characteristics and unadjusted odds ratio estimates of Hodgkin lymphoma for ever cigarette smokers versus never smokers and associated 95% confidence limits, according to substudy Data provider (substudy)
EBV status
Histology
Never smokers (cases/controls)
Ever cigarette smokers (cases/controls)
OR (95% CI)
Chang (Connecticut) Chang (Massachusetts) Cozen (USA) Dal Maso (Italy 1984–1991) Dal Maso (Italy 1998-2002) Epilymph (Spain) Epilymph (other centres) Glaser (Northern California) Hjalgrim (Denmark) Hjalgrim (Sweden) Kane (England)a Miligi (Italy) Monnereau (France) Spinelli (Canada) Zahm (Midwest) Pooled dataset
Yes Yes No No No Yes No Yes Yes Yes Yes No No No No –
Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes –
85/131 129/170 56/59 79/469 24/182 20/297 117/835 181/186 91/409 196/952 105/111 160/809 54/150 101/393 63/925 1461/6078
132/154 154/159 30/28 77/680 38/317 36/326 151/1053 129/139 147/722 137/1002 145/123 200/954 85/261 161/945 122/1337 1744/8200
1.40 (0.97–2.02) 1.34 (0.97–1.87) 1.14 (0.41–3.15) 0.74 (0.51–1.08) 1.79 (0.90–3.57) 1.29 (0.68–2.47) 1.03 (0.79–1.34) 0.99 (0.72–1.36) 1.63 (1.16–2.30) 1.08 (0.83–1.42) 1.43 (0.97–2.10) 0.97 (0.75–1.25) 0.89 (0.61–1.30) 1.10 (0.83–1.44) 1.31 (0.94–1.83) 1.13 (1.02–1.26)b
a
Not all cases were matched to a control. These excess cases only contributed to the case-series analyses. I is 6.
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number of siblings, history of infectious mononucleosis, and type of home in childhood (single-family or multiunit). Not all variables were available from all studies. For details on data collection and definitions, see references to the individual studies given in Table 1. The categorization of relative socioeconomic status was left to the contributing studies, most often based on education (see original references). HL was classified histologically according to the Revised European-American classification of Lymphoid Neoplasms and/or the 2001 World Health Organization (WHO) Classification of Hematopoietic and Lymphoid Tumors [12, 22, 24, 31], the Rye classification [16, 30, 32], or ICD for Oncology First [33, 34], Second [17, 33], or Third Editions [9, 26]. HL EBV status typing was assessed by standard immunohistochemistry for latent membrane protein (LMP)-1 and/or Epstein–Barr nuclear antigen (EBNA) and/or by in situ hybridization for EBV-encoded RNA (EBER) (Spanish EpiLymph study and [12, 17, 22, 26]). A priori it was decided to use random effects models to model variation between studies and localities. As some HL subtype outcomes were available only from a few large studies, some of these were split along state borders to allow better estimation of the random variation between studies, whereas some studies composed of methodologically similar contributions from many small study centers were aggregated to increase the likelihood of being able to provide parameter estimates from each locality. The resulting coding of study locality can be found in Table 2 under the heading ‘substudy’. We used the methodology suggested by Stukel et al. [35] to adjust the parameter estimates from each substudy for available confounders, rather than a minimal subset of globally available and semantically homogeneous confounders. All confounders were treated as covariates, except race which was coded by indicator variables for being Black, Asian, or Hispanic. Differences in confounder adjustments between studies might
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EBV status-specific HL, taking histology into account’, respectively. We aimed to correctly apportion etiologic heterogeneity in the response to smoking to the correlated outcomes positive EBV status and mixed cellularity histology. All analyses were carried out using a home-grown macro in SAS version 9.1.3 [39]. For observations analyzed by unconditional logistic regression, this macro avoided enormous or infinite estimates using techniques inspired by Clogg et al. [40] and Heinze and Schemper [41]. In the setting of multiple interest parameters, we assumed the random effects for each independent of the others. Fitting these models, we constrained each random component to have an arbitrarily selected variance of at least 0.0001. Details of the macro are available from the authors. We do not present estimates of the between-studies variation, as they were quite low. Tests for between-study variation were done by naïve likelihood-ratio tests, halving the P-value [42].
We obtained data from 12 studies delivered by 11 different providers (Table 1). In total, 3335 HL cases and 14 278 controls were included in the pooled dataset. Data were obtained mainly from frequency or individually matched studies, using population controls [12, 16, 17, 22, 26, 36, 37, 43], hospital controls [9, 31, 32], or both [24, 31]. We also included one twin study in which unaffected twins of cases were used as controls [30]. Data were separated into the 15 substudies covering substantially different geographical areas or time periods as shown in Table 2. Fourteen and seven substudies contained data on histology and EBV status, respectively. Further details can be found in the supplementary Table S1, available at Annals of Oncology online. Overall, 54.5% of cases and 57.4% of controls were ever cigarette smokers, corresponding to a slightly elevated adjusted OR of HL of 1.10 (95% CI 1.01–1.21) (Table 3). In Table 3, OR estimates of HL risk according to smoking variables, tumor EBV status, and histology are shown. Overall, compared with never cigarette smokers (OR = 1), current cigarette smoking was associated with a suggestive increased relative risk of HL overall (OR = 1.16, 95% CI 0.98–1.36), whereas the relative risk of HL overall among former cigarette smokers was not increased (OR = 1.00, 95% CI 0.84–1.21). The increased overall HL risk in current cigarette smokers reflected increased risks of mixed cellularity (OR = 1.60, 95% CI 1.29–1.99) and EBV-positive cHL (OR = 1.81, 95% CI 1.27– 2.56), and inconspicuous risks for nodular sclerosis (OR = 1.09, 95% CI 0.90–1.32) and EBV-negative cHL (OR = 1.02, 95% CI 0.72–1.44) (Table 3). The risks for mixed cellularity (OR = 1.21, 95% CI 0.95–1.54) and EBV-positive cHL (OR = 1.28, 95% CI 0.93–1.78) among former cigarette smokers compared with never cigarette smokers were elevated without being statistically significant (Table 3). Compared with never cigarette smokers, increased risks for mixed cellularity and EBV-positive cHL among current cigarette smokers were seen in both younger and older adults and among men (Table 3). Among female current cigarette smokers, the risk of EBV-positive (OR = 1.27, 95% CI 0.82– 1.96) and mixed cellularity cHL (OR = 1.23, 95% CI 0.86–1.77)
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discussion In the present analyses, self-reported history of cigarette smoking was associated with a marginally increased risk of HL overall. This is consistent with a recent large cohort study among UK women [44], and of two recent meta-analyses [27, 28] which also included case–control studies contributing to the present investigation. We also carried out detailed case–control and case-series analyses stratified according to tumor and patient characteristics separately and combined to explore the possibility of etiological heterogeneity between HL subtypes. These stratified analyses demonstrated that current cigarette smoking was associated with increased risks of mixed cellularity and EBV-positive cHL across all age groups, whereas the risks of nodular sclerosis and EBV-negative cHL were not systematically increased. These differences by tumor histology and EBV status were largely corroborated in the case-series analyses, which moreover indicated different associations with smoking when simultaneously considered. Thereby, we extend the findings of the two recent meta-analyses, which found stronger associations of smoking with risk of mixed cellularity [28] and EBV-positive HL [27]. While nuances in the association with smoking were apparent in analyses stratified by age and sex, the results suggest that, among persons with cHL, smokers and nonsmokers differ with respect to the histological phenotype and EBV status of their disease. This could imply that the smoking association may pertain predominantly or even exclusively to certain HL subtypes. Methodological issues and possible causal mechanisms must be considered to explain the observed associations. Involving data from 12 separate studies and with little evidence of statistical heterogeneity between studies, the observed associations are not likely to be chance findings. A variety of biases could have produced spurious associations consistent with the ones observed. These include participation bias, i.e.
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results
was elevated without being statistically significant in comparison to never smokers (Table 3). Compared with the case–control analyses, case-series comparisons found slightly weaker associations for the association of current cigarette smoking both with mixed cellularity versus nodular sclerosis and with EBV positivity versus EBV negativity (Table 3). Taking the correlation between EBV status and histology into account further reduced the associations, leaving current cigarette smoking positively associated only with EBV-positive cHL (Table 3). In the categorical analyses, statistically significant differences between strata were observed for all variables except years since cessation, when using never smokers as reference group (Table 4). However, when never-smokers were excluded, no statistically significant dose–response trends could be found in the analyses restricted to current smokers. Estimates of between-study variation appeared to be quite low (data not shown). For 79% (257 of 324) of the entries in Tables 3 and 4, the between-studies-variation estimate was at our a priori-defined lower limit. In 10 instances, the test for homogeneity yielded a statistically significant P-value; 10 of 324 is less than the 5% expected by chance.
Mixed cellularity versus nodular sclerosisa
EBV positive versus EBV neg.
EBV positive versus EBV neg.b
1.06 (0.89–1.26) 1.02 (0.72–1.44) 1.02 (0.79–1.33) 1 (ref.)
1.17 (0.95–1.45) 1.20 (0.95–1.52) 1.13 (0.86–1.49) 1 (ref.)
1.06 (0.79–1.42) 1.00 (0.71–1.41) 1.13 (0.78–1.62) 1 (ref.)
1.43 (1.06–1.93)A 1.63 (1.02–2.61)A 1.24 (0.86–1.79) 1 (ref.)
1.30 (0.99–1.71) 1.45 (1.02–2.05)A 1.11 (0.79–1.57) 1 (ref.)
1.85 (1.22–2.80)B 2.18 (1.42–3.36)C 1.36 (0.87–2.13) 1 (ref.)
1.09 (0.83–1.44) 1.15 (0.73–1.79) 1.01 (0.68–1.49) 1 (ref.)
1.16 (0.88–1.53) 1.20 (0.90–1.62) 1.07 (0.75–1.53) 1 (ref.)
0.95 (0.63–1.44) 0.98 (0.63–1.52) 0.92 (0.55–1.53) 1 (ref.)
1.67 (1.07–2.60)A 1.85 (1.08–3.18)A 1.27 (0.72–2.22) 1 (ref.)
1.44 (0.97–2.14) 1.51 (0.99–2.31) 1.19 (0.73–1.96) 1 (ref.)
0.99 (0.84–1.16) 1.04 (0.84–1.28) 0.97 (0.77–1.22) 1 (ref.)
1.20 (0.84–1.72) 1.27 (0.82–1.96) 1.16 (0.76–1.78) 1 (ref.)
1.04 (0.84–1.28) 1.04 (0.79–1.36) 1.08 (0.80–1.45) 1 (ref.)
1.12 (0.81–1.53) 1.09 (0.75–1.58) 1.23 (0.81–1.87) 1 (ref.)
1.03 (0.72–1.48) 0.93 (0.61–1.44) 1.15 (0.75–1.77) 1 (ref.)
1.18 (0.80–1.75) 1.21 (0.75–1.95) 1.17 (0.72–1.90) 1 (ref.)
1.08 (0.77–1.52) 1.14 (0.76–1.71) 1.03 (0.68–1.56) 1 (ref.)
1.28 (1.00–1.65) 1.33 (1.00–1.75)A 1.25 (0.89–1.77) 1 (ref.)
0.99 (0.86–1.13) 1.00 (0.82–1.22) 0.93 (0.70–1.23) 1 (ref.)
1.49 (1.08–2.05)A 1.44 (0.99–2.10) 1.49 (0.97–2.27) 1 (ref.)
0.94 (0.77–1.15) 0.87 (0.62–1.22) 0.91 (0.60–1.39) 1 (ref.)
1.23 (0.95–1.60) 1.22 (0.92–1.62) 1.32 (0.91–1.90) 1 (ref.)
1.05 (0.76–1.47) 1.00 (0.68–1.46) 1.19 (0.76–1.86) 1 (ref.)
1.67 (1.19–2.34)B 1.77 (1.17–2.67)B 1.52 (0.96–2.41) 1 (ref.)
1.41 (1.04–1.91)A 1.49 (1.05–2.11)A 1.23 (0.81–1.87) 1 (ref.)
1.43 (1.08–1.90)A 1.86 (1.34–2.59)C 1.18 (0.85–1.63) 1 (ref.)
1.23 (0.97–1.57) 1.50 (1.13–1.99)B 1.07 (0.82–1.40) 1 (ref.)
1.45 (0.95–2.21) 2.36 (1.38–4.02)B 1.11 (0.70–1.75) 1 (ref.)
1.46 (1.05–2.02)A 1.65 (1.04–2.61)A 1.34 (0.94–1.92) 1 (ref.)
1.03 (0.71–1.49) 1.07 (0.71–1.63) 0.97 (0.64–1.47) 1 (ref.)
0.93 (0.58–1.49) 0.94 (0.54–1.64) 0.92 (0.56–1.51) 1 (ref.)
1.04 (0.58–1.86) 1.50 (0.61–3.66) 0.97 (0.52–1.82) 1 (ref.)
1.01 (0.63–1.62) 1.15 (0.66–1.99) 0.93 (0.57–1.54) 1 (ref.)
1.58 (1.10–2.27)A 1.89 (1.27–2.81)B 1.43 (0.94–2.19) 1 (ref.)
1.05 (0.81–1.37) 1.07 (0.79–1.45) 1.04 (0.75–1.45) 1 (ref.)
1.34 (0.84–2.15) 1.35 (0.79–2.30) 1.18 (0.69–2.03) 1 (ref.)
0.98 (0.69–1.41) 1.05 (0.55–2.01) 1.03 (0.67–1.59) 1 (ref.)
1.14 (0.77–1.69) 1.15 (0.76–1.75) 1.12 (0.68–1.85) 1 (ref.)
1.05 (0.66–1.67) 0.98 (0.60–1.61) 1.19 (0.68–2.08) 1 (ref.)
1.31 (0.71–2.40) 1.36 (0.64–2.88) 1.19 (0.59–2.41) 1 (ref.)
1.10 (0.70–1.74) 1.13 (0.69–1.84) 1.03 (0.59–1.80) 1 (ref.)
1.26 (0.92–1.73) 1.39 (0.95–2.04) 1.06 (0.70–1.60) 1 (ref.)
1.11 (0.92–1.35) 1.17 (0.94–1.46) 1.02 (0.79–1.33) 1 (ref.)
1.43 (0.96–2.13) 1.70 (1.09–2.65)A 1.09 (0.66–1.82) 1 (ref.)
1.06 (0.80–1.40) 1.14 (0.83–1.59) 1.00 (0.70–1.43) 1 (ref.)
1.14 (0.82–1.59) 1.13 (0.79–1.62) 1.19 (0.75–1.89) 1 (ref.)
1.00 (0.68–1.47) 0.97 (0.64–1.48) 1.08 (0.64–1.82) 1 (ref.)
1.40 (0.91–2.16) 1.41 (0.88–2.27) 1.39 (0.77–2.52) 1 (ref.)
1.28 (0.89–1.86) 1.30 (0.87–1.95) 1.19 (0.72–1.97) 1 (ref.)
Nodular sclerosis
EBV positive
EBV negative
1.42 (1.17–1.72)C 1.60 (1.29–1.99)C 1.21 (0.95–1.54) 1 (ref.)
1.05 (0.94–1.18) 1.09 (0.90–1.32) 0.98 (0.84–1.14) 1 (ref.)
1.57 (1.20–2.05)B 1.81 (1.27–2.56)B 1.28 (0.93–1.78) 1 (ref.)
1.44 (1.13–1.84)B 1.68 (1.29–2.19)C 1.15 (0.86–1.55) 1 (ref.)
1.09 (0.92–1.29) 1.18 (0.93–1.50) 0.99 (0.80–1.23) 1 (ref.)
1.23 (0.91–1.65) 1.23 (0.86–1.77) 1.26 (0.87–1.82) 1 (ref.)
Annals of Oncology
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Smoking status Ever smokers 1.11 (1.01–1.22)A Current smokers 1.16 (0.99–1.37) Former smokers 1.00 (0.83–1.21) Never smokers 1 (ref.) Men Ever smokers 1.17 (1.00–1.38) Current smokers 1.26 (1.02–1.56)A Former smokers 1.01 (0.80–1.26) Never smokers 1 (ref.) Women Ever smokers 1.03 (0.90–1.18) Current smokers 1.07 (0.91–1.25) Former smokers 1.00 (0.81–1.23) Never smokers 1 (ref.) Age 15–44 years Ever smokers 1.02 (0.91–1.14) Current smokers 1.00 (0.85–1.19) Former smokers 1.01 (0.77–1.31) Never smokers 1 (ref.) Age 45+ years Ever smokers 1.31 (1.11–1.55)B Current smokers 1.66 (1.37–2.01)C Former smokers 1.08 (0.89–1.31) Never smokers 1 (ref.) Socioeconomic status 1 Ever smokers 1.09 (0.89–1.34) Current smokers 1.23 (0.99–1.54) Former smokers 0.94 (0.70–1.26) Never smokers 1 (ref.) Socioeconomic status 2 Ever smokers 1.11 (0.94–1.30) Current smokers 1.16 (0.97–1.39) Former smokers 1.07 (0.81–1.42) Never smokers 1 (ref.)
Case-series Mixed cellularity versus nodular sclerosis
Mixed cellularity
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Case–control All cases
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| Kamper-Jørgensen et al.
Table 3. Adjusted odds ratio estimates for Hodgkin lymphoma overall and for EBV and histology-specific Hodgkin lymphoma subtypes and associated 95% confidence limits based on case–control and case-series modeling according to cigarette smoking status by age and sex
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b
a
The association between smoking and risk of histology-specific cHL, taking EBV status into account. The association between smoking and risk of EBV status-specific cHL, taking histology into account. *P < 0.05; **P < 0.01; ***P < 0.001.
1.13 (0.84–1.52) 1.36 (0.87–2.12) 1.11 (0.79–1.56) 1 (ref.) 1.41 (0.89–2.22) 1.64 (0.87–3.06) 1.31 (0.78–2.19) 1 (ref.) 0.98 (0.78–1.23) 1.19 (0.86–1.66) 0.91 (0.69–1.22) 1 (ref.) 1.26 (0.88–1.79) 1.29 (0.81–2.05) 1.33 (0.87–2.04) 1 (ref.) Socioeconomic status 3 Ever smokers 1.06 (0.87–1.29) Current smokers 1.11 (0.83–1.50) Former smokers 0.99 (0.77–1.28) Never smokers 1 (ref.)
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overrepresentation of individuals with healthy lifestyles among population controls, possibly balanced by the opposite, i.e. overrepresentation of less healthy individuals in studies using hospital controls, and recall bias, i.e. a tendency for cases to be more likely than controls to recall, e.g. cigarette smoking. However, a number of results argue that these biases are unlikely to be primary explanations for the observed associations. In particular, the association with cigarette smoking was observed specifically for mixed cellularity and EBV-positive cHL, whereas there was no evidence of a consistent association with the larger groups of nodular sclerosis and EBV-negative cHL. Accordingly, there are no strong reasons to believe that participation or recall should differ substantially by histology or EBV status among cases in ways that would explain this. Moreover, there also was good agreement between the case–control and case-series comparisons, the latter being less likely to suffer from the aforementioned biases [38]. There is evidence that cigarette smoking is increasingly associated with lower socioeconomic status in many populations [45]. HL histology and EBV status also vary by socioeconomic status, although the relationship is rather complex and possibly subject to secular changes [5, 46, 47]. Therefore, although all analyses were adjusted for measures of socioeconomic status residual confounding may theoretically have contributed to our observations but is unlikely to account for them entirely. The increased risk of cHL observed among current cigarette smokers, which is potentially confined to mixed cellularity and/or EBV-positive variants therefore likely reflects a causal association. Biologically, this is plausible. HL, especially the EBV-positive subtype, occurs with increased frequency upon immune incompetence [48, 49], and it is well established that tobacco smoking affects the immune system detrimentally in numerous ways [27, 50]. Altered Th1/Th2 balance, known to be associated with smoking and with HL, might be a possible causal mechanism to explain the association [30, 48]. Epidemiologically, it is also noteworthy that tobacco smoking has been associated with titers of antiEBV viral capsid antigen immunoglobulin G antibodies [51], which in turn is associated with an increased risk of EBVpositive HL [52]. In contrast to the recent meta-analyses [27, 28], we found no compelling evidence of monotonic dose–response associations between smoking and cHL risk. These conflicting study-specific findings may reflect differences in the analytical approach regarding the reference group. In the present investigation, trend estimates were assessed only within the group of current smokers, whereas, in the meta-analyses, the group of neversmokers was included in the trend estimates [27, 28]. Accordingly, when we mimicked this analytical approach, we too obtained a statistically significant trend in relative risk of all subtypes of HL combined among current smokers of similar magnitude as that of the meta-analyses [27, 28] (data not shown). However, this approach entirely disregards basic and possible smoking unrelated differences between ever and never smokers (in technical terms, the intercept in the statistical models), which was not statistically acceptable in all subanalyses (data not shown).
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1.21 (0.83–1.77) 1.21 (0.77–1.92) 1.23 (0.76–2.02) 1 (ref.)
1.04 (0.67–1.61) 1.03 (0.58–1.80) 1.03 (0.62–1.71) 1 (ref.)
1.28 (0.76–2.14) 1.66 (0.81–3.43) 1.09 (0.59–2.02) 1 (ref.)
1.09 (0.72–1.67) 1.26 (0.73–2.16) 1.00 (0.61–1.64) 1 (ref.)
Annals of Oncology
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Case–control All cases
Mixed cellularity versus nodular sclerosisa
EBV positive versus EBV negative
EBV positive versus EBV negativeb
1.07 (0.65–1.75) 1.10 (0.84–1.44) 1.07 (0.69–1.66) 1 (ref.) 1.00 (0.93–1.07)
1.24 (0.84–1.85) 1.18 (0.89–1.56) 1.25 (0.74–2.12) 1 (ref.) 1.01 (0.96–1.05)
1.19 (0.73–1.92) 0.90 (0.59–1.37) 0.98 (0.47–2.04) 1 (ref.) 1.01 (0.96–1.07)
1.49 (0.91–2.45) 1.66 (1.03–2.68)A 2.22 (1.12–4.39)A 1 (ref.) 1.02 (0.96–1.07)
1.26 (0.79–1.99) 1.43 (0.95–2.17) 1.75 (0.88–3.49) 1 (ref.) 1.00 (0.95–1.06)
1.31 (0.69–2.47) 2.32 (1.49–3.60)C 2.09 (1.15–3.80)A 1 (ref.) 1.15 (0.61–2.19)
1.09 (0.71–1.68) 1.41 (1.04–1.91)A 1.47 (0.98–2.22) 1 (ref.) 0.90 (0.51–1.60)
0.90 (0.58–1.41) 1.20 (0.87–1.66) 1.37 (0.98–1.92) 1 (ref.) 1.48 (0.99–2.22)
0.67 (0.33–1.36) 0.95 (0.60–1.50) 1.07 (0.62–1.83) 1 (ref.) 1.31 (0.68–2.51)
1.66 (0.86–3.21) 1.58 (1.00–2.48) 1.28 (0.73–2.23) 1 (ref.) 1.23 (0.50–3.03)
1.52 (0.84–2.74) 1.48 (0.97–2.26) 1.17 (0.70–1.98) 1 (ref.) 0.93 (0.49–1.76)
1.06 (0.80–1.41) 0.98 (0.82–1.18) 1.45 (1.09–1.92)A 1 (ref.) 0.97 (0.80–1.18)
1.71 (0.97–3.00) 1.37 (0.89–2.11) 2.29 (1.40–3.73)B 1 (ref.) 1.05 (0.76–1.45)
1.08 (0.69–1.69) 0.89 (0.67–1.19) 1.35 (0.79–2.33) 1 (ref.) 1.07 (0.81–1.41)
1.23 (0.79–1.91) 1.21 (0.90–1.62) 1.22 (0.82–1.83) 1 (ref.) 1.06 (0.74–1.53)
1.09 (0.62–1.90) 0.87 (0.58–1.31) 1.19 (0.65–2.17) 1 (ref.) 1.03 (0.66–1.62)
1.96 (1.11–3.48)A 1.62 (1.07–2.46)A 1.38 (0.77–2.48) 1 (ref.) 0.86 (0.52–1.43)
1.42 (0.85–2.36) 1.36 (0.93–1.99) 1.40 (0.79–2.49) 1 (ref.) 0.96 (0.62–1.48)
1.15 (0.96–1.39) 1.19 (0.93–1.52) 1.21 (0.88–1.67) 1 (ref.) 0.99 (0.98–1.01)
1.47 (0.91–2.36) 2.69 (1.57–4.60)C 2.38 (1.18–4.81)A 1 (ref.) 1.01 (0.99–1.03)
1.22 (0.90–1.65) 1.45 (0.98–2.15) 1.43 (0.72–2.85) 1 (ref.) 1.00 (0.98–1.02)
1.04 (0.76–1.42) 1.33 (0.91–1.94) 1.34 (0.87–2.08) 1 (ref.) 1.01 (0.99–1.02)
0.72 (0.45–1.14) 1.31 (0.73–2.35) 1.06 (0.50–2.26) 1 (ref.) 1.01 (0.98–1.03)
1.70 (0.95–3.01) 1.21 (0.67–2.21) 1.58 (0.67–2.72) 1 (ref.) 1.01 (0.97–1.04)
1.52 (1.00–2.30) 1.14 (0.64–2.00) 1.47 (0.70–3.11) 1 (ref.) 1.00 (0.97–1.02)
Nodular sclerosis EBV positive
EBV negative
1.82 (1.26–2.62)B 1.64 (1.26–2.14)C 1.34 (0.90–2.00) 1 (ref.) 0.97 (0.91–1.03)
1.04 (0.82–1.31) 1.16 (0.97–1.39) 1.03 (0.78–1.37) 1 (ref.) 0.98 (0.94–1.03)
1.89 (1.18–3.01)B 1.66 (1.08–2.54)A 2.35 (1.38–4.00)B 1 (ref.) 0.96 (0.88–1.05)
1.17 (0.75–1.82) 1.80 (1.32–2.46)C 1.68 (1.18–2.39)B 1 (ref.) 1.33 (0.94–1.89)
1.25 (0.98–1.59) 1.15 (0.93–1.41) 1.11 (0.86–1.42) 1 (ref.) 0.82 (0.59–1.15)
1.08 (0.68–1.71) 1.43 (1.04–1.97)A 2.17 (1.52–3.09)C 1 (ref.) 1.24 (0.97–1.59)
1.11 (0.81–1.53) 2.00 (1.27–3.15)B 2.34 (1.60–3.42)C 1 (ref.) 1.01 (0.99–1.02)
Annals of Oncology
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Initiation age (years)c ≤14 1.08 (0.84–1.38) 15–19 1.24 (1.09–1.42)B ≥20 1.00 (0.80–1.26) Never smokers 1 (ref.) ‘Trend per 0.99 (0.97–1.01) year’ Cigarettes per dayc 1–9 1.07 (0.89–1.30) 10–19 1.25 (1.04–1.51)A ≥20 1.26 (1.08–1.48)B Never smokers 1 (ref.) ‘Trend per 20 1.03 (0.85–1.24) cigarettes’ Smoking duration (years)c 0–9 0.96 (0.75–1.23) 10–29 1.06 (0.92–1.22) ≥30 1.68 (1.39–2.02)C Never smokers 1 (ref.) ‘Trend per10 1.15 (1.00–1.33) years’ Pack yearsc ≤14 1.05 (0.91–1.22) 15–29 1.37 (1.09–1.73)B ≥30 1.65 (1.34–2.03)C Never smokers 1 (ref.) ‘Trend per 1.01 (1.00–1.01) year’
Case series Mixed cellularity versus nodular sclerosis
Mixed cellularity
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| Kamper-Jørgensen et al.
Table 4. Adjusted odds ratio estimates of Hodgkin lymphoma and associated 95% confidence limits according to smoking status, EBV status, and histology, stratified on age and sex. Based on case–control and case-series modeling, respectively
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b
a
The association between smoking and risk of histology-specific cHL, taking EBV status into account. The association between smoking and risk of EBV status-specific cHL, taking histology into account. c Among current smokers. d Among former smokers. *P < 0.05; **P < 0.01; ***P < 0.001.
1.28 (0.79–2.09) 0.72 (0.37–1.41) 1.19 (0.77–1.83) 1 (ref.) 1.05 (0.80–1.38) 0.79 (0.46–1.34) 0.81 (0.38–1.73) 0.95 (0.67–1.33) 1 (ref.) 1.14 (0.93–1.40) 1.11 (0.50–2.46) 1.24 (0.59–2.59) 1.09 (0.65–1.83) 1 (ref.) 1.12 (0.87–1.44) 1.08 (0.80–1.47) 0.84 (0.60–1.18) 0.85 (0.65–1.09) 1 (ref.) 0.86 (0.72–1.03) 1.77 (1.12–2.81)A 0.87 (0.48–1.57) 0.98 (0.68–1.42) 1 (ref.) 0.92 (0.75–1.13) Years since cessationd 0–4 1.22 (0.98–1.52) 5–9 0.89 (0.69–1.14) ≥10 0.83 (0.68–1.01) Never smokers 1 (ref.) ‘Trend per 10 0.84 (0.71–1.00)A years’
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The absence of an effect of age at cigarette-smoking initiation, pack-years, and duration of cigarette smoking in the present study could also be explained if smoking only influenced one or more late stages in lymphomagenesis [20]. In the extreme, this would imply that only the cigarette-smoking pattern immediately before symptomatic cancer development would influence the risk pattern. The age-specific incidence pattern for HL, with its large fraction of cases in adolescents and younger adults, and evidence that the interval from EBV primary infection to diagnosis of EBV-positive HL may be on the order of a few years [53, 54], make it plausible that recent exposures could be particularly important to risk. This mechanism would explain the absence of dose–response associations between HL risk and age at initiation of cigarette smoking, duration of cigarette smoking, and cumulative cigarette-smoking exposure, and would be consistent with former cigarette smoking being a less strong risk factor for HL than current cigarette smoking. Technically, the inconspicuous trend in HL risk with increasing cigarette smoking intensity could also be due to inadequate statistical power alone or in combination with inhomogeneous exposure information in the contributing studies. Accordingly, despite the large number of cases included, only 197 current cigarette smokers with mixed cellularity HL and 108 current cigarette smokers with EBV-positive HL were available for the dose–response subanalyses. Stratified analyses showed that smoking history differed between different strata of patients and HL subtypes. The association was stronger in men than in women, stronger in older than in younger adults, stronger for mixed cellularity than for nodular sclerosis cHL, and stronger for EBV-positive than for EBV-negative cHL. The latter variation by EBV status potentially could explain the other differences. Accordingly, the proportion of EBV-positive cases is consistently higher in men than in women, higher in older than in younger adults, and higher in mixed cellularity cHL than in NS cHL [6]. To disentangle the different associations, we carried out case-series analyses that included both cHL histological subtype and EBV status. Albeit with limited statistical precision, these analyses indicated that the association with current cigarette smoking was governed more by HL EBV status than by HL histology. This adds further to the two recent meta-analyses [27, 28], which did not conduct analyses stratified by both histology and EBV status. However, not all observations were consistent with a restriction of association to EBV-positive HL. Specifically, increased risks were seen across all subtypes of cHL in currently smoking older adults, and accordingly, in this age group, no differences between cHL variants were seen in the case-series analyses. Moreover, though elevated risk estimates were observed for both mixed cellularity and EBV-positive cHL in women, neither of these was formally statistically significant. It therefore remains to be determined whether these findings represent chance variations and/or age and sex-dependent variation in the effect of smoking on HL risk such that, in etiological investigations, HL should be stratified by combining sex, age, histology, and viral status. Among the strengths of our investigation is the large number of cases and controls, allowing for analyses stratified by HL subtype as well as both case–control and case-series
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1.14 (0.46–2.80) 0.71 (0.27–1.84) 1.21 (0.69–2.13) 1 (ref.) 0.99 (0.72–1.34)
1.92 (0.81–4.52) 1.22 (0.53–2.81) 1.38 (0.77–2.46) 1 (ref.) 1.04 (0.70–1.57)
1.55 (0.69–3.48) 0.82 (0.35–1.94) 1.31 (0.75–2.29) 1 (ref.) 1.08 (0.79–1.46)
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acknowlegements The authors thank the following funders for contribution to individual studies: NIH, Nordic Cancer Union, Plan Danmark, and the Italian Association for Cancer Research.
funding The study was supported by NIH (grant number 5 ROI CA 69269), Nordic Cancer Union (no grant number), Plan
| Kamper-Jørgensen et al.
Danmark grant number is 16-02-D, and the Italian Association for Cancer Research (no grant number).
disclosure The authors have declared no conflicts of interest.
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comparisons. In addition, all studies except one [30] included patients with incident HL, and all cases were classified according to standard criteria. Misclassification may nevertheless have occurred between histological subtypes of HL, most likely so that cases of nodular sclerosis cHL were classified as another HL subtype [55, 56]. However, such misclassification cannot explain the association observed with history of cigarette smoking for HL overall, nor for mixed cellularity and EBVpositive cHL. Another limitation lies in the use of self-reported smoking data. Across studies, history of cigarette smoking was assessed using identically structured questionnaires in cases and controls, and self-reported smoking status has previously been found to be reasonably reliable [57] and valid [58]. However, the covariates available from the included studies may not have been optimal to assess the etiologic association with HL risk. In particular, smoking intensity, derived as an average over a smoking career may be quite different from current or recent smoking intensity, which may be the causally more relevant exposure. Most likely, this misclassification would have driven the observed association with smoking intensity toward the null. Meta-regression of study characteristics on the outcomes from each substudy for some of the most frequent HL outcomes suggested that traditional study characteristics, such as types of controls and prevalence of exposure in controls, were of little importance for the presented results. However, this analysis also suggested some heterogeneity in the way ever cigarette smoking HL cases were classified into former and current smokers, and that this classification occasionally could not avoid reverse causality. Thus, for some American substudies, we found current smoking to be protective, while former smoking increased HL risk. For most European substudies, former smoking seemed protective, which was also counterintuitive (data not shown). Very detailed information about the timing of case-definition, interview, exposure cessation, etc., or prospective studies seems to be the way to avoid this problem in future studies. To summarize, we observed a positive association between current smoking and risk of cHL, which applied to all subtypes as defined by histology, age, and EBV status, respectively, with the exceptions of nodular sclerosis and EBV-negative cHL in younger adults, respectively. This finding identifies tobacco smoking to be a strong candidate as a modifiable risk factor for a subset of cHL. Importantly, our analyses also support previous speculations of etiological heterogeneity in cHL, which emphasizes the need to take subtype markers into consideration in future investigations.
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