Skip to main content
PMC home page
British Journal of Cancer logoLink to British Journal of Cancer
. 2012 Aug 9;107(5):879–887. doi: 10.1038/bjc.2012.333

Alcohol drinking, tobacco smoking and subtypes of haematological malignancy in the UK Million Women Study

M E Kroll 1,*, F Murphy 1, K Pirie 1, G K Reeves 1, J Green 1, V Beral 1, for the Million Women Study Collaborators
PMCID: PMC3425977  PMID: 22878373

Abstract

Background:

Previous research suggests associations of lower alcohol intake and higher tobacco consumption with increased risks of haematological malignancy. The prospective Million Women Study provides sufficient power for reliable estimates of subtype-specific associations in women.

Methods:

Approximately 1.3 million middle-aged women were recruited in the United Kingdom during 1996–2001 and followed for death, emigration and cancer registration until 2009 (mean 10.3 years per woman); potential risk factors were assessed by questionnaire. Adjusted relative risks were estimated by Cox regression.

Results:

During follow-up, 9162 incident cases of haematological malignancy were recorded, including 7047 lymphoid and 2072 myeloid cancers. Among predominantly moderate alcohol drinkers, higher intake was associated with lower risk of lymphoid malignancies, in particular diffuse large B-cell lymphoma (relative risk 0.85 per 10 g alcohol per day (95% confidence interval 0.75–0.96)), follicular lymphoma (0.86 (0.76–0.98)) and plasma cell neoplasms (0.86 (0.77–0.96)). Among never- and current smokers, higher cigarette consumption was associated with increased risk of Hodgkin lymphoma (1.45 per 10 cigarettes per day (1.22–1.72)), mature T-cell malignancies (1.38 (1.10–1.73)) and myeloproliferative/myelodysplastic disease (1.42 (1.31–1.55)).

Conclusion:

These findings confirm and extend existing evidence for associations of subtypes of haematological malignancy with two common exposures in women.

Keywords: alcohol, smoking, lymphoma, leukaemia, plasma cell neoplasms, myelodysplastic/myeloproliferative neoplasms

Haematological malignancies are cancers that originate from lymphoid or myeloid cells and affect blood, bone marrow and lymph nodes. The tenth revision of the International Classification of Diseases (ICD-10) groups cases primarily by clinical presentation (leukaemia, myeloma or lymphoma). In contrast, the third edition of the International Classification of Diseases for Oncology (ICD-O-3) groups haematological malignancies primarily by cell lineage (lymphoid or myeloid), and includes some myeloid neoplasms that are not coded as malignant in ICD-10 (Jaffe et al, 2001; Swerdlow, 2008). A hierarchical classification based on ICD-O-3 has been used in recent international collaborative studies (Morton et al, 2007; Sant et al, 2010; Turner et al, 2010).

Alcohol drinking and tobacco smoking are modifiable exposures that are widespread in developed countries. Both are known to be associated with risks of certain types of haematological malignancy. Several recent cohort studies have reported decreasing trends in risk of non-Hodgkin lymphoma (NHL) and/or diffuse large B-cell lymphoma (a major subtype of NHL) with increasing alcohol intake among drinkers (Lim et al, 2007; Allen et al, 2009; Kanda et al, 2010; Troy et al, 2010). Smoking is considered to be causally related to myeloid leukaemia in adults (IARC, 2002), and (comparing current with never-smokers) has been associated with increased risk of Hodgkin lymphoma, acute myeloid leukaemia and myelodysplastic syndromes in recent cohort (Fernberg et al, 2007; Lim et al, 2007; Nieters et al, 2008; Ma et al, 2009, 2010) and case–control (Kasim et al, 2005; Besson et al, 2006a) studies. However, haematological malignancies are probably heterogeneous in aetiology, and much of the evidence for subtype-specific associations remains inconclusive, perhaps reflecting inconsistent exposure classifications, or relatively small study sizes.

We examined association of subtypes of haematological malignancy with alcohol drinking and tobacco smoking in the prospective Million Women Study. To aid comparison with previous research, we report findings based on both ICD-O-3 and ICD-10. The large size of this cohort provides sufficient power to estimate risk for relatively rare subtypes.

Materials and methods

Definitions

The Million Women Study has been described elsewhere (Reeves et al, 2007). Between 1996 and 2001, with appropriate ethical approval, 1.3 million middle-aged women were recruited through breast cancer screening clinics in the United Kingdom. Participants gave written informed consent, and completed questionnaires recording personal and lifestyle characteristics (available at www.millionwomenstudy.org). By linkage to the National Health Service Central Registers, participants are followed for death, emigration and cancer registration. Each incident neoplasm is coded using the combination of a disease code from ICD-10 and a morphology code from either the second or the third (ICD-O-3) edition of the International Classification of Diseases for Oncology.

For this analysis, haematological neoplasms were defined by the following ICD-10 codes: C81-C96 (malignant neoplasms of lymphoid, haematopoietic and related tissue), D45 (polycythemia vera), D46 (myelodysplastic syndromes) and D47 (other neoplasms of uncertain or unknown behaviour of lymphoid, haematopoietic and related tissue). Women were excluded if they had been diagnosed before recruitment with any haematological neoplasm (as defined above), or cancer of any other site except non-melanoma skin cancer (all other ICD-10 C codes except C44), or in situ breast carcinoma (ICD-10 D05), using equivalent definitions from earlier standard coding systems where necessary. For the remaining women, observation extended from the date of recruitment to the date of death, emigration, diagnosis with any of the neoplasms listed in the exclusion criteria, or end of follow-up, whichever occurred first. Follow-up ended on 31 December 2008 for Scotland and the North West (Merseyside and Cheshire) cancer registry region, and 31 December 2009 elsewhere.

Morphology codes for incident haematological neoplasms were converted from the second to the third edition as necessary (National Cancer Institute, 2011). Where the morphology code was uninformative (80001/80003, N=115) or discrepant (N=3), it was changed to match the ICD-10 code, unless both were non-specific (N=10). Malignant disease was defined and classified in two different ways (Table 1). ICD-O-3 morphology codes with fifth digit 3 (malignant) and first three digits in the range 959–998 (haematological neoplasms), except 975 (histiocytic and dendritic neoplasms, N=2), were grouped using a classification adapted from the InterLymph hierarchical scheme for epidemiological research (Turner et al, 2010) and the Haemacare project (Sant et al, 2010). Cases were grouped primarily by cell lineage. Subtypes of mature B-cell lymphoid malignancy included plasma cell neoplasms and ‘CLL/SLL’ (cases coded as either chronic lymphocytic leukaemia or small lymphocytic lymphoma, now considered to be a single disease). Hodgkin lymphoma formed a separate subtype of lymphoid malignancy. Myeloid malignancies were divided into two subtypes: acute myeloid leukaemia and ‘myeloproliferative/myelodysplastic disease’ (myeloproliferative and myelodysplastic neoplasms, including chronic myeloid leukaemia). For comparison, cases with ICD-10 codes C81-C96 were classified as Hodgkin lymphoma (C81), NHL (C82-C85, C96), ‘myeloma’ (multiple myeloma, plasma cell neoplasms and malignant immunoproliferative diseases (C88, C90)) and leukaemia (C91-C95).

Table 1. Number of women diagnosed with haematological neoplasms during follow-up: cross-classification by ICD-O-3 and ICD-10.

Classification ICD-O-3 ICD-10
Term All specified codes have 5th digit 3 (malignant behaviour) HL C81 NHL C82-C85 C96 MMC88, C90 Leuk C91-C95 Oth D45-D47 Total
Lymphoid malignancies 959–973, 976, 982–983, 9940, 9948 287 4226 1597 937 0 7047
 Hodgkin lymphoma 965–966 287 0 0 0 0 287
 Mature B cell 967–969 except (9675), 973, 976, 9823, 9826, 9833, 9940            
  Diffuse large B cell   9678, 9679, 9680, 9684 0 1151 1 0 0 1152
  Follicular lymphoma   969 except 9699 0 1027 0 0 0 1027
  Plasma cell neoplasms   973 0 0 1518 0 0 1518
  CLL/SLL   9670, 9823 0 133 0 787 0 920
  Other/unspec. mat. B cell   9671, 9673, 9687, 9689, 9699, 976, 9826, 9833, 9940 0 368 78 30 0 476
 Mature T cell 970–971, 9827, 9831, 9834, 9948 0 194 0 3 0 197
 Other/unspec. lymphoid 959, 9675, 972, 9820, 983 except (9831, 9833, 9834) 0 1353 0 117 0 1470
               
Myeloid malignancies 974, 984–998 except (9940, 9948) 0 8 0 831 1233 2072
 Acute myeloid leukaemia 984–993 except (9860, 9863, 9875, 9876), 9984 0 0 0 614 3 617
 Myeloprolif./dysplastic dis. 974, 9863, 9875, 9876, 9945, 9946, 995–998 except 9984 0 8 0 192 1230 1430
 Other/unspec. myeloid 9860 0 0 0 25 0 25
               
Unspecified lineage 980 0 0 0 43 0 43
               
Not haematological cancer 800, 975, any code with 5th digit not 3 0 12 0 0 243 255
All haematological neoplasms   287 4246 1597 1811 1476 9417
Open in a new tab

Abbreviations: CLL/SLL=chronic lymphocytic leukaemia/small lymphocytic lymphoma; HL=Hodgkin lymphoma; ICD-O-3=International Classification of Diseases for Oncology 3rd edition; ICD-10=International Classification of Diseases 10th revision; Leuk=leukaemia; MM=myeloma (multiple myeloma, plasma cell neoplasms and malignant immunoproliferative diseases); Myeloprolif./dysplastic dis.=myeloproliferative/myelodysplastic disease (including chronic myeloid leukaemia); NHL=non-Hodgkin lymphoma; Oth=other haematological neoplasms.

Statistical analysis

Relative risk was estimated by Cox regression, taking attained age as the underlying time variable, and stratifying by cancer registry region of residence at recruitment (i.e. assuming equal coefficients across strata but with a baseline hazard unique to each stratum) (StataCorp., 2009). The proportional hazards assumption was examined using Schoenfeld residuals and found acceptable. To assess the possibility that associations might reflect lifestyle changes caused by subclinical disease (reverse causation), all analyses were repeated without the first 3 years of follow-up. All statistical tests were two-sided and used the 5% significance level.

Categorical exposure measures were derived from information given on the questionnaire completed by each woman at the time of recruitment to the study, as follows: current weekly alcohol consumption (none, 0.5–<3, 3–<7, ⩾7 drinks, in units equivalent to approximately 10 g of pure alcohol); tobacco smoking (past, never, current <15 cigarettes per day, current ⩾15 cigarettes per day); socioeconomic status (within-study quintiles of the 1991 Townsend deprivation index for the census enumeration district or output area containing the woman’s home address at recruitment (Townsend, 1988)); body mass index (<25, 25–<30, ⩾30 kg m−2); height (<160, 160–<165, ⩾165 cm). The questions relevant to smoking habits were ‘About how many cigarettes do you smoke on average each day, now?’ and ‘Are you an ex-smoker?’ (yes/no). Body mass index (Reeves et al, 2007) and height (Green et al, 2011) were known to be associated with risks of haematological malignancy in this cohort.

In turn, each of the two factors of interest (alcohol and smoking) was treated as the main explanatory variable, with all the other exposure measures acting as adjustment factors. Women with missing information for the explanatory variable were excluded from that analysis; those with missing information for an adjustment factor were included as a separate category of the adjustment factor. Trends were assessed by allocating a score to each category of the explanatory variable, and fitting log-linear models to the change in hazard ratio per unit increase in score. Current non-drinkers were excluded from the alcohol trend model, and ex-smokers from the smoking trend model, because reasons for abstention might include ill-health. As an approximate correction for regression dilution bias (Macmahon et al, 1990), each category of drinking and smoking was scored as the mean daily intake reported at re-survey approximately 3 years after recruitment among women in that category: drinks in units of approximately 10 g pure alcohol per day (0.26, 0.75, 1.63) and smoking in multiples of 10 cigarettes per day (0, 1.10, 2.00), scoring self-reported never-smokers at recruitment as zero. Heterogeneity of trends between diagnostic groups was assessed by a χ2 contrast test (Smith-Warner et al, 2006).

Results

Descriptive statistics

The number of women who were eligible for these analyses was 1 319 121, after excluding 45 035 with neoplasms diagnosed before recruitment. On average, women were aged 56.6 years at recruitment, and contributed 10.3 person-years to the analyses.

The number of women diagnosed with haematological malignancies during follow-up was 9162 according to the hierarchical classification based on ICD-O-3, and 7941 according to the simple ICD-10 grouping (Table 1); the main reason for the difference was that clinical behaviour for 1230 cases of myeloproliferative/myelodysplastic disease was treated as malignant in ICD-O-3 but uncertain or unknown in ICD-10. Of the 5093 mature B-cell cases, 2679 (53%) were coded in ICD-10 as NHL, 1597 (31%) as myeloma and 817 (16%) as leukaemia.

Information on alcohol consumption at recruitment was obtained from 1 308 786 women (99%), of whom 994 030 (76%) reported ⩾0.5 drinks per week (Table 2). Among drinkers at recruitment, the mean intake reported at re-survey was 5.6 drinks per week, a moderate level by national standards (Office for National Statistics, 2003). Of the 1 241 605 women (94%) who could be classified as never, current or past smokers at recruitment, 21% (255 148) were current smokers and 28% (352 493) were past smokers. The measure of socioeconomic status was available for 1 309 534 women (99%). Height was reported by 98% of women, and both height and weight (enabling calculation of body mass index) by 95%.

Table 2. Characteristics of the women included in these analyses.

  Alcohol
Smoking
 
  Non-drinkers Drinkers a Past Never Current All women
Number of women 314 756 994 030 352 493 633 964 255 148 1 319 121
             
Characteristics at recruitment            
 % Drinkersa – – 81 76 70 76
 % Current smokers 26 19 – – – 21
 % Lower socioeconomic statusb 45 30 33 27 48 33
 Body mass index (kg m−2): mean (s.d.) 27.2 (5.4) 25.9 (4.4) 26.7 (4.8) 26.2 (4.6) 25.6 (4.5) 26.2 (4.7)
 Height (cm): mean (s.d.) 161.2 (6.9) 162.2 (6.7) 162.3 (6.7) 162.0 (6.7) 161.5 (6.8) 162.0 (6.7)
 Age (years): mean (s.d.) 57.3 (4.9) 56.4 (4.8) 56.8 (4.9) 56.8 (4.9) 55.8 (4.5) 56.6 (4.9)
             
Follow-up            
 Woman-years observed (1000s) 3218.0 10270.0 3622.3 6608.7 2562.9 13593.7
 Number of incident cases: ICD-O–3 2469 6617 2532 4312 1797 9162
 Number of incident cases: ICD-10 2152 5726 2191 3808 1487 7941
Open in a new tab

Abbreviations: ICD-O-3=International Classification of Diseases for Oncology 3rd edition; ICD-10=International Classification of Diseases 10th revision.

a

⩾0.5 drinks per week, in units equivalent to 10 g pure alcohol.

b

Highest within-study tertile of the 1991 Townsend deprivation index for the census enumeration district or output area of the home address.

Socioeconomic status, body mass index, height and age varied with alcohol and tobacco intake (Table 2). The proportion of participants with relatively low socioeconomic status was smaller for drinkers than non-drinkers, and greater for current smokers than never-smokers. On average, drinkers were slightly leaner, taller and younger than non-drinkers, and current smokers were slightly leaner, shorter and younger than never-smokers.

Alcohol

Using the ICD-O-3 classification, and taking occasional drinkers (0.5–<3 drinks per week) as the reference group, the estimated relative risk of haematological malignancy was 0.90 (95% confidence interval 0.85–0.95) for ⩾7 drinks per week (Table 3). Among drinkers, there was a statistically significant decreasing trend with increasing alcohol intake (Ptrend<0.001; Table 3); the estimated relative risk for an increase of 10 g per day was 0.92 (0.89–0.96) (Figure 1). In more detail, there was a statistically significant decreasing trend for the lymphoid subgroup (Ptrend<0.001) and no apparent trend for the myeloid subgroup, although the test for heterogeneity between lymphoid and myeloid trends was not statistically significant (Phet=0.09; Figure 1). Among specified subtypes of lymphoid malignancy, there was a statistically significant trend only for mature B-cell disease (Ptrend<0.001), within which there were similar significant decreasing trends for diffuse large B-cell lymphoma, follicular lymphoma and plasma cell neoplasms, but not CLL/SLL; the test for heterogeneity was not statistically significant (Phet=0.1). For Hodgkin lymphoma, the risk was significantly higher in non-drinkers than in occasional drinkers (relative risk 1.70 (1.27–2.26); Table 3); a similar result was obtained when the first 3 years of follow-up were excluded (data not shown).

Table 3. Association of alcohol drinking with risk of haematological malignancies.

  All women Non-drinkers
0.5–<3 drinks per week
3–<7 drinks per week
⩾7 drinks per week
Trend among drinkers
Alcohol a Cases Cases RR 95% CI Cases Ref Cases RR 95% CI Cases RR 95% CI Cases P trend
All haematological malignancies
 ICD-O-3 classification 9086 2469 1.05 0.99, 1.10 3364 1.00 1384 0.91 0.85, 0.96 1869 0.90 0.85, 0.95 6617 <0.001
 ICD-10 classification 7878 2152 1.06 1.00, 1.12 2924 1.00 1201 0.90 0.84, 0.96 1601 0.89 0.83, 0.94 5726 <0.001
                             
Subgroups of ICD-O-3 classification
 ICD-O-3 haematological malignanciesb                            
  Lymphoid 6990 1912 1.06 1.00, 1.12 2602 1.00 1067 0.90 0.84, 0.97 1409 0.88 0.82, 0.94 5078 <0.001
  Myeloid 2053 546 1.01 0.91, 1.14 745 1.00 311 0.93 0.81, 1.06 451 0.99 0.88, 1.11 1507 0.8
 ICD-O-3 lymphoid malignancies                            
  Hodgkin lymphoma 281 108 1.70 1.27, 2.26 85 1.00 31 0.79 0.53, 1.20 57 1.06 0.76, 1.49 173 0.6
  Mature B cell 5056 1344 1.01 0.94, 1.08 1925 1.00 763 0.87 0.80, 0.95 1024 0.87 0.81, 0.94 3712 <0.001
  Mature T cell 196 40 0.78 0.53, 1.15 71 1.00 35 1.04 0.70, 1.57 50 1.05 0.73, 1.52 156 0.7
  Other/unspecified lymphoid 1457 420 1.18 1.03, 1.34 521 1.00 238 1.00 0.85, 1.16 278 0.84 0.73, 0.98 1037 0.02
 ICD-O-3 mature B-cell malignancies                            
  Diffuse large B-cell lymphoma 1145 331 1.07 0.93, 1.24 443 1.00 153 0.77 0.64, 0.92 218 0.82 0.69, 0.96 814 0.01
  Follicular lymphoma 1021 255 0.96 0.82, 1.13 389 1.00 171 0.94 0.78, 1.13 206 0.82 0.69, 0.98 766 0.02
  Plasma cell neoplasms 1506 406 1.00 0.88, 1.14 587 1.00 223 0.84 0.72, 0.98 290 0.82 0.71, 0.95 1100 0.006
  CLL/SLL 911 245 1.10 0.93, 1.30 327 1.00 148 1.00 0.82, 1.22 191 0.97 0.81, 1.16 666 0.6
  Other/unspecified mature B-cell 473 107 0.83 0.65, 1.06 179 1.00 68 0.84 0.64, 1.12 119 1.11 0.88, 1.40 366 0.4
 ICD-O-3 myeloid malignanciesc                            
  Acute myeloid leukaemia 613 165 0.99 0.81, 1.21 233 1.00 89 0.84 0.66, 1.08 126 0.89 0.71, 1.11 448 0.3
  Myeloproliferative/myelodysplastic disease 1415 372 1.02 0.89, 1.17 503 1.00 220 0.98 0.83, 1.15 320 1.03 0.89, 1.19 1043 0.7
                             
Subgroups of ICD-10 classification
 ICD-10 haematological malignancies                            
  Hodgkin lymphoma 281 108 1.70 1.27, 2.26 85 1.00 31 0.79 0.53, 1.20 57 1.06 0.76, 1.49 173 0.6
  Non-Hodgkin lymphoma 4216 1132 1.02 0.95, 1.11 1593 1.00 652 0.89 0.82, 0.98 839 0.84 0.77, 0.92 3084 <0.001
  Myeloma 1584 422 0.99 0.88, 1.13 616 1.00 231 0.83 0.72, 0.97 315 0.85 0.74, 0.98 1162 0.02
  Leukaemia 1797 490 1.12 0.99, 1.26 630 1.00 287 1.01 0.87, 1.16 390 1.01 0.89, 1.15 1307 0.9
Open in a new tab

Abbreviations: Cases=number of incident cases; CI=confidence interval; CLL/SLL=chronic lymphocytic leukaemia/small lymphocytic lymphoma; ICD-O-3=International Classification of Diseases for Oncology 3rd edition; ICD-10=International Classification of Diseases 10th revision; Ptrend=result of test for categorical trend per 10 g per day; Ref=referent; RR=relative risk.

Myeloproliferative/myelodysplastic disease includes chronic myeloid leukaemia.

a

Reported alcohol consumption at recruitment, in units of approximately 10 g pure alcohol. RR estimates are adjusted for body mass index, height, smoking and socioeconomic status, and stratified by cancer registry region. Follow-up starts at recruitment.

b

Excludes 43 unspecified cases.

c

Excludes 25 other/unspecified cases.

Figure 1.

Figure 1

Open in a new tab

Association of alcohol drinking and tobacco smoking with risk of haematological malignancies. Million Women Study, United Kingdom 1996–2009. Relative risks are adjusted for body mass index, height and socioeconomic status (and for alcohol consumption and smoking where not the factor of interest) and stratified by cancer registry region. Follow-up starts at recruitment. Abbreviations: ICD-O-3=International Classification of Diseases for Oncology 3rd edition; ICD-10=International Classification of Diseases 10th revision; CLL/SLL=chronic lymphocytic leukaemia/small lymphocytic lymphoma; Cases=number of incident cases; CI=confidence interval; Phet=result of χ2 contrast test for heterogeneity of trends between subtypes. Myeloproliferative/myelodysplastic disease includes chronic myeloid leukaemia.

Using the ICD-10 classification, there were significant decreasing trends for NHL and myeloma separately, and little evidence of association for leukaemia or Hodgkin lymphoma, although the test for heterogeneity of trends between diagnostic groups was not statistically significant (Phet=0.06) (Figure 1).

Excluding the first 3 years of follow-up made little difference to the trend estimates, but changed results from non-significant to significant for the tests of heterogeneity between lymphoid and myeloid malignancies (Phet=0.01) and between mature B-cell subtypes (Phet=0.01) (Table 5).

Smoking

Using the ICD-O-3 classification, and taking women who had never smoked as the reference group, the estimated relative risk of haematological malignancy for frequent smokers (⩾15 cigarettes per day) was 1.30 (1.20–1.40) (Table 4). There were statistically significant trends in both lymphoid (1.07 (1.03–1.12)) and myeloid (1.33 (1.24–1.42)) disease, with strong evidence of heterogeneity between these groups (Phet<0.001) (Figure 1). There was also strong evidence of heterogeneity within lymphoid disease (Phet<0.001), with statistically significant increasing trends for Hodgkin lymphoma (1.45 (1.22–1.72)) and mature T-cell malignancies (1.38 (1.10–1.73)) but not for mature B-cell malignancies. There was heterogeneity between subgroups of myeloid malignancy (Phet=0.001), with a statistically significant trend for myeloproliferative/myelodysplastic disease (1.42 (1.31–1.55)) but not for acute myeloid leukaemia (1.10 (0.96–1.26)). Comparing frequent smokers with never-smokers, the estimated relative risks of Hodgkin lymphoma, mature T-cell malignancies and myeloproliferative/myelodysplastic disease were each approximately doubled (2.19 (1.56–3.09), 2.09 (1.33–3.26) and 1.98 (1.67–2.35), respectively) (Table 4).

Table 4. Association of tobacco smoking with risk of haematological malignancies.

  All women Ex-smokers
Never-smokers
<15 cigarettes per day
≥15 cigarettes per day
Trend among never- or current smokers
Smoking a Cases Cases RR 95% CI Cases Ref Cases RR 95% CI Cases RR 95% CI Cases P trend
All haematological malignancies
 ICD-O-3 classification 8641 2532 1.09 1.04, 1.15 4312 1.00 896 1.13 1.05, 1.22 901 1.30 1.20, 1.40 6109 <0.001
 ICD-10 classification 7486 2191 1.07 1.01, 1.13 3808 1.00 747 1.06 0.98, 1.15 740 1.19 1.10, 1.29 5295 <0.001
                             
Subgroups of ICD-O-3 classification                            
 ICD-O-3 haematological malignanciesb                            
  Lymphoid 6638 1938 1.06 1.01, 1.13 3394 1.00 653 1.04 0.96, 1.14 653 1.18 1.09, 1.29 4700 <0.001
  Myeloid 1963 582 1.19 1.07, 1.32 902 1.00 238 1.46 1.26, 1.69 241 1.69 1.46, 1.96 1381 <0.001
 ICD-O-3 lymphoid malignancies                            
  Hodgkin lymphoma 263 60 0.90 0.66, 1.23 122 1.00 31 1.30 0.87, 1.94 50 2.19 1.56, 3.09 203 <0.001
  Mature B cell 4803 1427 1.07 1.00, 1.14 2498 1.00 452 0.98 0.89, 1.09 426 1.06 0.96, 1.18 3376 0.5
  Mature T cell 184 61 1.38 0.98, 1.93 78 1.00 17 1.15 0.68, 1.95 28 2.09 1.33, 3.26 123 0.006
  Other/unspecified lymphoid 1388 390 1.05 0.93, 1.19 696 1.00 153 1.20 1.01, 1.44 149 1.31 1.10, 1.58 998 0.001
 ICD-O-3 mature B-cell malignancies                            
  Diffuse large B-cell lymphoma 1084 332 1.11 0.96, 1.27 560 1.00 99 0.97 0.78, 1.21 93 1.04 0.83, 1.30 752 0.7
  Follicular lymphoma 976 283 1.07 0.92, 1.24 497 1.00 94 1.00 0.80, 1.25 102 1.22 0.98, 1.52 693 0.1
  Plasma cell neoplasms 1425 428 1.08 0.96, 1.22 750 1.00 140 1.03 0.86, 1.24 107 0.91 0.74, 1.12 997 0.4
  CLL/SLL 870 252 1.02 0.87, 1.19 461 1.00 84 1.00 0.79, 1.26 73 1.01 0.78, 1.29 618 0.9
  Other/unspecified mature B cell 448 132 1.02 0.82, 1.27 230 1.00 35 0.79 0.55, 1.13 51 1.32 0.96, 1.80 316 0.3
 ICD-O-3 myeloid malignanciesc                            
  Acute myeloid leukaemia 586 172 1.08 0.89, 1.31 291 1.00 70 1.29 0.99, 1.68 53 1.08 0.80, 1.46 414 0.2
  Myeloproliferative/myelodysplastic disease 1353 404 1.24 1.09, 1.41 602 1.00 163 1.52 1.27, 1.81 184 1.98 1.67, 2.35 949 <0.001
                             
Subgroups of ICD-10 classification
 ICD-10 haematological malignancies                            
  Hodgkin lymphoma 263 60 0.90 0.66, 1.23 122 1.00 31 1.30 0.87, 1.94 50 2.19 1.56, 3.09 203 <0.001
  Non-Hodgkin lymphoma 4011 1176 1.08 1.01, 1.16 2024 1.00 392 1.05 0.94, 1.17 419 1.26 1.14, 1.41 2835 <0.001
  Myeloma 1496 450 1.08 0.96, 1.21 789 1.00 144 1.01 0.84, 1.21 113 0.92 0.75, 1.12 1046 0.3
  Leukaemia 1716 505 1.06 0.95, 1.19 873 1.00 180 1.12 0.95, 1.32 158 1.11 0.93, 1.32 1211 0.1
Open in a new tab

Abbreviations: Cases=number of incident cases; CI=confidence interval; CLL/SLL=chronic lymphocytic leukaemia/small lymphocytic lymphoma; ICD-O-3=International Classification of Diseases for Oncology 3rd edition; ICD-10=International Classification of Diseases 10th revision; Ptrend=result of test for categorical trend per 10 cigarettes per day; Ref=referent; RR=relative risk.

Myeloproliferative/myelodysplastic disease includes chronic myeloid leukaemia.

a

Reported smoking habit at recruitment. RR estimates are adjusted for body mass index, height, alcohol consumption and socioeconomic status, and stratified by cancer registry region. Follow-up starts at recruitment.

b

Excludes 40 unspecified cases.

c

Excludes 24 other/unspecified cases.

Using the ICD-10 classification, there was strong evidence of heterogeneity between subgroups (Phet<0.001), with statistically significant increasing trends for Hodgkin lymphoma and NHL but not for myeloma or leukaemia (Figure 1).

Excluding the first 3 years of follow-up made little difference to the trend estimates, and did not affect the conclusions of the tests for heterogeneity (Table 5).

Table 5. Association of alcohol drinking and tobacco smoking with risk of haematological malignancies: trend analysis excluding the first 3 years of follow-up.

  Alcohol Smoking
  Trend among drinkers a
Trend among never- or current smokers b
Excluding first 3 years of follow-up Cases RR 95% CI P trend Cases RR 95% CI P trend
All haematological malignancies
 ICD-O-3 classification 5235 0.94 0.89, 0.98 0.006 4825 1.14 1.09, 1.18 <0.001
 ICD-10 classification 4528 0.92 0.87, 0.97 0.001 4170 1.07 1.03, 1.12 0.001
                 
Subgroups of ICD-O-3 classification
 ICD-O-3 haematological malignanciesc Phet=0.01 Phet<0.001
  Lymphoid 4031 0.91 0.86, 0.96 <0.001 3721 1.07 1.02, 1.12 0.006
  Myeloid 1177 1.04 0.95, 1.15 0.4 1082 1.36 1.26, 1.47 <0.001
 ICD-O-3 lymphoid malignancies Phet=0.2 Phet<0.001
  Hodgkin lymphoma 129 1.07 0.79, 1.44 0.7 152 1.57 1.29, 1.90 <0.001
  Mature B cell 3045 0.90 0.84, 0.95 <0.001 2774 1.00 0.95, 1.06 0.9
  Mature T cell 118 1.19 0.88, 1.61 0.3 88 1.34 1.02, 1.77 0.03
  Other/unspecified lymphoid 739 0.88 0.78, 1.00 0.05 707 1.17 1.06, 1.30 0.002
 ICD-O-3 mature B-cell malignancies Phet=0.01 Phet=0.2
  Diffuse large B-cell lymphoma 699 0.83 0.72, 0.95 0.005 650 1.03 0.92, 1.16 0.6
  Follicular lymphoma 590 0.88 0.76, 1.02 0.08 542 1.08 0.96, 1.22 0.2
  Plasma cell neoplasms 901 0.83 0.74, 0.94 0.002 820 0.92 0.83, 1.02 0.1
  CLL/SLL 540 0.97 0.83, 1.12 0.6 494 0.96 0.84, 1.10 0.6
  Other/unspecified mature B cell 315 1.19 0.99, 1.43 0.07 268 1.10 0.93, 1.30 0.3
 ICD-O-3 myeloid malignanciesc Phet=0.4 Phet<0.001
  Acute myeloid leukaemia 343 0.98 0.81, 1.17 0.8 311 1.07 0.91, 1.26 0.4
  Myeloproliferative/myelodysplastic disease 821 1.07 0.95, 1.21 0.2 759 1.48 1.35, 1.62 <0.001
                 
Subgroups of ICD-10 classification                
 ICD-10 haematological malignancies Phet=0.06 Phet<0.001
  Hodgkin lymphoma 129 1.07 0.79, 1.44 0.7 152 1.57 1.29, 1.90 <0.001
  Non-Hodgkin lymphoma 2435 0.89 0.83, 0.96 0.001 2227 1.12 1.05, 1.18 <0.001
  Myeloma 947 0.86 0.76, 0.96 0.008 856 0.92 0.83, 1.02 0.1
  Leukaemia 1017 1.03 0.93, 1.14 0.6 935 1.03 0.94, 1.13 0.5
Open in a new tab

Abbreviations: Cases, number of incident cases; CI, confidence interval; CLL/SLL, chronic lymphocytic leukaemia/small lymphocytic lymphoma; ICD-O-3, International Classification of Diseases for Oncology 3rd edition; ICD-10, International Classification of Diseases 10th revision; Phet result of χ2 contrast test for heterogeneity of trends between subtypes; Ref, referent; Ptrend, result of test for categorical trend; RR, relative risk.

Myeloproliferative/myelodysplastic disease includes chronic myeloid leukaemia.

a

Relative risk per 10 g per day, adjusted for body mass index, height, smoking and socioeconomic status, and stratified by cancer registry region.

b

Relative risk per 10 cigarettes per day, adjusted for body mass index, height, alcohol consumption and socioeconomic status, and stratified by cancer registry region.

c

Excludes other/unspecified cases.

Discussion

Alcohol

In this cohort, most women who drank alcohol were moderate drinkers. Among the drinkers, greater alcohol intake was associated with significantly reduced risks of diffuse large B-cell lymphoma, follicular lymphoma and plasma cell neoplasms, lymphoid and mature B-cell disease overall, but not other specified lymphoid subtypes or myeloid malignancies. Although there was no statistically significant heterogeneity between subtypes in the main analysis, significant heterogeneity between lymphoid and myeloid malignancies, and between mature B-cell subtypes, emerged when the first 3 years of follow-up were excluded. Thus some of the apparent lack of heterogeneity might be due to reverse causation. Using the ICD-10 classification, there were significant inverse associations with risks of NHL and myeloma, but not leukaemia or Hodgkin lymphoma.

Previous cohort studies have reported a statistically significant decreasing trend with greater alcohol intake among drinkers for diffuse large B-cell lymphoma (Lim et al, 2007; Troy et al, 2010), and no significant trend for Hodgkin lymphoma, follicular lymphoma, plasma cell neoplasms or any other non-Hodgkin subtype examined (Lim et al, 2007; Kanda et al, 2010; Troy et al, 2010), although one study found a near-significant decreasing trend for plasma cell neoplasms (Troy et al, 2010); comparable results from case–control studies were generally consistent with these findings (Morton et al, 2005b; Besson et al, 2006a, 2006b). In studies that estimated risks in drinkers relative to non-drinkers, similar results were obtained for lymphoid subtypes (Kanda et al, 2009; Chang et al, 2010) except for one observation of increased CLL/SLL risk in drinkers (Chang et al, 2010), and there was no trend in risk of acute myeloid leukaemia (Ma et al, 2010) or myelodysplastic syndromes (Ma et al, 2009). A multi-centre case–control study reported lower risk of Hodgkin lymphoma in ever-regular drinkers compared with never-regular drinkers, based on 222 cases (Besson et al, 2006a). In our study, with 281 cases in total, the risk of Hodgkin lymphoma was estimated to be lower among current occasional drinkers (at recruitment) than in current non-drinkers, but there was no significant trend in risk with increasing intake; excluding the first 3 years of follow-up did not change either of these findings.

Using a similar ICD-10 classification, an earlier analysis of data from the Million Women Study with 7.2 years of follow-up on average (Allen et al, 2009) reported a statistically significant inverse association in drinkers for NHL, but not for myeloma or leukaemia; the significant association with myeloma seen in the analysis reported here probably reflects the larger number of cases accumulated over a longer follow-up period. These results are broadly consistent with comparable findings from other cohort (Chiu et al, 1999; Klatsky et al, 2009) and case–control (Gorini et al, 2007a, 2007b) studies.

Our findings strengthen existing evidence for an association of greater alcohol intake with reduced risk of diffuse large B-cell lymphoma among drinkers, and demonstrate similar associations for two further subtypes of lymphoid disease: follicular lymphoma and plasma cell neoplasms. Further work is needed to elucidate potential biological mechanisms; for example, the role of chronic inflammation (Smedby et al, 2008; Chang et al, 2010).

Smoking

We found statistically significant increasing trends in risk of Hodgkin lymphoma, mature T-cell malignancies and myeloproliferative/myelodysplastic disease with increasing current cigarette consumption relative to never-smokers (approximately double risk for women who reported smoking ⩾15 cigarettes per day), but no significant trends for mature B-cell malignancy or any of its subtypes. Tests for heterogeneity between diagnostic subgroups were highly significant. The trend estimate for acute myeloid leukaemia, although above unity, was not statistically significant. Using the ICD-10 classification, there were significant increasing trends for Hodgkin lymphoma and NHL but not for myeloma or leukaemia. Excluding the first 3 years of follow-up did not affect these conclusions.

Recent cohort studies, also comparing current smokers with never-smokers, have reported statistically significant associations for Hodgkin lymphoma (Lim et al, 2007; Nieters et al, 2008), myelodysplastic syndromes (Ma et al, 2009) and acute myeloid leukaemia (Fernberg et al, 2007; Ma et al, 2010), but no association for T-cell malignancies or any other non-Hodgkin subtype examined (Lim et al, 2007; Nieters et al, 2008; Troy et al, 2010; Lu et al, 2011). Results from case–control studies were similar: comparing current with never-smokers there were significant positive associations for Hodgkin lymphoma (Besson et al, 2006a) and acute myeloid leukaemia (Kasim et al, 2005), but no association for T-cell malignancies or any other non-Hodgkin subtype examined (Morton et al, 2005a; Besson et al, 2006b) except for one positive association for follicular lymphoma (Morton et al, 2005a); comparing ever- with never-smokers there were significant positive associations for myelodysplastic syndromes (Nisse et al, 2001; Strom et al, 2005). Trend analyses including former smokers suggested (in a cohort study) an inverse association with follicular lymphoma (Lim et al, 2007) and (in a case–control study) a positive association with Hodgkin lymphoma (Kanda et al, 2009).

Our findings support existing evidence for associations of smoking with Hodgkin lymphoma and myelodysplastic syndromes (a subset of myeloproliferative/myelodysplastic disease), and demonstrate a similar association for mature T-cell malignancies. Tobacco smoke contains benzene and other known leukaemogens, and it has been concluded that there is ‘sufficient evidence in humans’ that tobacco smoking causes myeloid (but not lymphoid) leukaemia (IARC, 2002). Hence, smoking is a plausible cause of myeloproliferative/myelodysplastic disease, which includes chronic myeloid leukaemia and various myeloid pre-leukaemic conditions. An association with T-cell disease has not been reported before, to our knowledge, perhaps because this is a relatively rare diagnostic group. It has been suggested that smoking might impair the T-cell-mediated immune response to Epstein–Barr virus infection, a putative causal factor for Hodgkin lymphoma (Nieters et al, 2008), and it is tempting to speculate that such a process might also promote T-cell malignancies.

Strengths and limitations

This very large prospective study clarifies and extends existing evidence for associations of subtypes of haematological malignancy with alcohol and tobacco consumption, using two different current classification systems. Exposures were reported by the study participants at recruitment, and follow-up for death, emigration and cancer registration was virtually complete. Estimates were mutually adjusted for alcohol and smoking, socioeconomic status, body mass index and height. Reverse causation is an unlikely explanation for the associations seen, as excluding the first 3 years of follow-up did not qualitatively change the results.

Although the analysis was stratified by cancer registry region of residence at recruitment, variation in diagnostic and coding practice remains a possible source of bias. The registries adopted ICD-O-3 during the study period, at times that differed between regions, and previous ascertainment of myeloproliferative/myelodysplastic disease is likely to have been incomplete (Office for National Statistics, 2010). Subclassification of lymphoma may sometimes have been inaccurate (Clarke et al, 2004, 2006) and was often imprecise. Conceivably, time to diagnosis might be associated with the factors of interest: for example, causes of mediastinal symptoms of Hodgkin lymphoma might perhaps be investigated more rapidly in smokers because of the well-known risk of lung disease in smokers. However, the strong and highly significant associations reported are unlikely to be due to coding problems, or to chance.

Conclusions

Relative risks associated with alcohol and tobacco consumption among middle-aged women in the United Kingdom were estimated for subtypes of haematological malignancy. Among predominantly moderate drinkers, greater alcohol intake was associated with reduced risk of lymphoid malignancies: in particular, diffuse large B-cell lymphoma, consistent with previous reports, and follicular lymphoma and plasma cell neoplasms (not previously reported, to our knowledge). Cigarette smoking was associated with increased risk of Hodgkin lymphoma, consistent with previous reports, mature T-cell malignancies (not previously reported, to our knowledge) and myeloproliferative/myelodysplastic disease (previously reported for myelodysplastic syndromes, but not for the grouping used here).

Footnotes

This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License.

References

  1. Allen NE, Beral V, Casabonne D, Kan SW, Reeves GK, Brown A, Green J (2009) Moderate alcohol intake and cancer incidence in women. J Natl Cancer Inst 101(5): 296–305 [DOI] [PubMed] [Google Scholar]
  2. Besson H, Brennan P, Becker N, De Sanjose S, Nieters A, Font R, Maynadie M, Foretova L, Cocco PL, Staines A, Vornanen M, Boffetta P (2006a) Tobacco smoking, alcohol drinking and Hodgkin's lymphoma: a European multi-centre case-control study (EPILYMPH). Br J Cancer 95(3): 378–384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Besson H, Brennan P, Becker N, Nieters A, De Sanjose S, Font R, Maynadie M, Foretova L, Cocco PL, Staines A, Vornanen M, Boffetta P (2006b) Tobacco smoking, alcohol drinking and non-Hodgkin's lymphoma: A European multicenter case-control study (Epilymph). Int J Cancer 119(4): 901–908 [DOI] [PubMed] [Google Scholar]
  4. Chang ET, Clarke CA, Canchola AJ, Lu Y, Wang SS, Ursin G, West DW, Bernstein L, Horn-Ross PL (2010) Alcohol consumption over time and risk of lymphoid malignancies in the California Teachers Study cohort. Am J Epidemiol 172(12): 1373–1383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chiu BCH, Cerhan JR, Gapstur SM, Sellers TA, Zheng W, Lutz CT, Wallace RB, Potter JD (1999) Alcohol consumption and non-Hodgkin lymphoma in a cohort of older women. Br J Cancer 80(9): 1476–1482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Clarke CA, Glaser SL, Dorfman RF, Bracci PM, Eberle E, Holly EA (2004) Expert review of non-Hodgkin's lymphomas in a population-based cancer registry: reliability of diagnosis and subtype classifications. Cancer Epidemiol Biomarkers Prev 13(1): 138–143 [DOI] [PubMed] [Google Scholar]
  7. Clarke CA, Undurraga DM, Harasty PJ, Glaser SL, Morton LM, Holly EA (2006) Changes in cancer registry coding for lymphoma subtypes: reliability over time and relevance for surveillance and study. Cancer Epidemiol Biomarkers Prev 15(4): 630–638 [DOI] [PubMed] [Google Scholar]
  8. Fernberg P, Odenbro A, Bellocco R, Boffetta P, Pawitan Y, Zendehdel K, Adami J (2007) Tobacco use, body mass index, and the risk of leukemia and multiple myeloma: a nationwide cohort study in Sweden. Cancer Res 67(12): 5983–5986 [DOI] [PubMed] [Google Scholar]
  9. Gorini G, Stagnaro E, Fontana V, Miligi L, Ramazzotti V, Amadori D, Rodella S, Tumino R, Crosignani P, Vindigni C, Fontana A, Vineis P, Seniori Costantini A (2007a) Alcohol consumption and risk of Hodgkin's lymphoma and multiple myeloma: a multicentre case-control study. Ann Oncol 18(1): 143–148 [DOI] [PubMed] [Google Scholar]
  10. Gorini G, Stagnaro E, Fontana V, Miligi L, Ramazzotti V, Nanni O, Rodella S, Tumino R, Crosignani P, Vindigni C, Fontana A, Vineis P, Costantini AS (2007b) Alcohol consumption and risk of leukemia: A multicenter case-control study. Leuk Res 31(3): 379–386 [DOI] [PubMed] [Google Scholar]
  11. Green J, Cairns BJ, Casabonne D, Wright FL, Reeves G, Beral V (2011) Height and cancer incidence in the Million Women Study: prospective cohort, and meta-analysis of prospective studies of height and total cancer risk. Lancet Oncol 12(8): 785–794 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. IARC (2002) IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. Tobacco Smoke and Involuntary Smoking. Vol. 83. International Agency for Research on Cancer: Lyon [PMC free article] [PubMed] [Google Scholar]
  13. Jaffe ES, Harris NL, Stein H, Vardiman JW (eds) (2001) World Health Organization Classification of Tumours: Pathology and Genetics of Tumours of Haematopoietic and Lymphoid Tissues. IARC Press: Lyon, France [Google Scholar]
  14. Kanda J, Matsuo K, Inoue M, Iwasaki M, Sawada N, Shimazu T, Yamaji T, Sasazuki S, Tsugane S. Japan Public Health Center-based Prospective Study G (2010) Association of alcohol intake with the risk of malignant lymphoma and plasma cell myeloma in Japanese: a population-based cohort study (Japan Public Health Center-based Prospective Study). Cancer Epidemiol Biomarkers Prev 19(2): 429–434 [DOI] [PubMed] [Google Scholar]
  15. Kanda J, Matsuo K, Kawase T, Suzuki T, Ichinohe T, Seto M, Morishima Y, Tajima K, Tanaka H (2009) Association of alcohol intake and smoking with malignant lymphoma risk in Japanese: a hospital-based case-control study at Aichi Cancer Center. Cancer Epidemiol Biomarkers Prev 18(9): 2436–2441 [DOI] [PubMed] [Google Scholar]
  16. Kasim K, Levallois P, Abdous B, Auger P, Johnson KC (2005) Lifestyle factors and the risk of adult leukemia in Canada. Cancer Causes Control 16(5): 489–500 [DOI] [PubMed] [Google Scholar]
  17. Klatsky AL, Li Y, Baer D, Armstrong MA, Udaltsova N, Friedman GD (2009) Alcohol Consumption and Risk of Hematologic Malignancies. Ann Epidemiol 19(10): 746–753 [DOI] [PubMed] [Google Scholar]
  18. Lim U, Morton LM, Subar AF, Baris D, Stolzenberg-Solomon R, Leitzmann M, Kipnis V, Mouw T, Carroll L, Schatzkin A, Hartge P (2007) Alcohol, smoking, and body size in relation to incident Hodgkin’s and non-Hodgkin’s lymphoma risk. Am J Epidemiol 166(6): 697–708 [DOI] [PubMed] [Google Scholar]
  19. Lu Y, Wang SS, Reynolds P, Chang ET, Ma H, Sullivan-Halley J, Clarke CA, Bernstein L (2011) Cigarette smoking, passive smoking, and non-Hodgkin lymphoma risk: evidence from the California Teachers Study. Am J Epidemiol 174(5): 563–573 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ma X, Lim U, Park Y, Mayne ST, Wang R, Hartge P, Hollenbeck AR, Schatzkin A (2009) Obesity, lifestyle factors, and risk of myelodysplastic syndromes in a large US cohort. Am J Epidemiol 169(12): 1492–1499 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ma X, Park Y, Mayne ST, Wang R, Sinha R, Hollenbeck AR, Schatzkin A, Cross AJ (2010) Diet, lifestyle, and acute myeloid leukemia in the NIH-AARP cohort. Am J Epidemiol 171(3): 312–322 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Macmahon S, Peto R, Cutler J, Collins R, Sorlie P, Neaton J, Abbott R, Godwin J, Dyer A, Stamler J (1990) Blood-pressure, stroke, and coronary heart-disease.1. Prolonged differences in blood-pressure - prospective observational studies corrected for the regression dilution bias. Lancet 335(8692): 765–774 [DOI] [PubMed] [Google Scholar]
  23. Morton LM, Hartge P, Holford TR, Holly EA, Chiu BCH, Vineis P, Stagnaro E, Willett EV, Franceschi S, La Vecchia C, Hughes AM, Cozen W, Davis S, Severson RK, Bernstein L, Mayne ST, Dee FR, Cerhan JR, Zheng TZ (2005a) Cigarette smoking and risk of non-Hodgkin lymphoma: A pooled analysis from the international lymphoma epidemiology consortium (InterLymph). Cancer Epidemiol Biomarkers Prev 14(4): 925–933 [DOI] [PubMed] [Google Scholar]
  24. Morton LM, Turner JJ, Cerhan JR, Linet MS, Treseler PA, Clarke CA, Jack A, Cozen W, Maynadié M, Spinelli JJ, Costantini AS, Rüdiger T, Scarpa A, Zheng T, Weisenburger DD (2007) Proposed classification of lymphoid neoplasms for epidemiologic research from the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph). Blood 110(2): 695–708 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Morton LM, Zheng T, Holford TR, Holly EA, Chiu BC, Costantini AS, Stagnaro E, Willett EV, Dal Maso L, Serraino D, Chang ET, Cozen W, Davis S, Severson RK, Bernstein L, Mayne ST, Dee FR, Cerhan JR, Hartge P, InterLymph C (2005b) Alcohol consumption and risk of non-Hodgkin lymphoma: a pooled analysis. Lancet Oncology 6(7): 469–476 [DOI] [PubMed] [Google Scholar]
  26. National Cancer Institute. Surveillance Reasearch Program: ICD conversion programs. seer.cancer.gov/tools/conversion/ICDO2-3.xls (accessed 25 March 2011)
  27. Nieters A, Rohrmann S, Becker N, Linseisen J, Ruediger T, Overvad K, Tjonneland A, Olsen A, Allen NE, Travis RC, Bingham S, Khaw KT, Ardanaz E, Redondo ML, Basterrechea M, Martinez C, Tormo MJ, Rosso S, Tagliabue G, Masala G, Mattiello A, Tumino R, Boeing H, Bergmann M, Kaaks R, Trichopoulou A, Trichopoulos D, Peeters PH, Bueno-de-Mesquita B, Boffetta P, Brennan P, Ferrari P, Neasham D, Lund E, Berglund G, Manjer J, Hallmans G, Johansson I, Vineis P, Riboli E (2008) Smoking and lymphoma risk in the European prospective investigation into cancer and nutrition. Am J Epidemiol 167(9): 1081–1089 [DOI] [PubMed] [Google Scholar]
  28. Nisse C, Haguenoer JM, Grandbastien B, Preudhomme C, Fontaine B, Brillet JM, Lejeune R, Fenaux P (2001) Occupational and environmental risk factors of the myelodysplastic syndromes in the North of France. Br J Haematol 112(4): 927–935 [DOI] [PubMed] [Google Scholar]
  29. Office for National Statistics (2003) The National Diet and Nutrition Survey: Adults Aged 19 to 64 years. Vol. 2. HMSO: London [Google Scholar]
  30. Office for National Statistics (2010) Cancer Statistics Registrations, England (Series MB1). No. 39, 2008. HMSO: London [Google Scholar]
  31. Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D (2007) Cancer incidence and mortality in relation to body mass index in the Million Women Study: Cohort study. Br Med J 335(7630): 1134–1139 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Sant M, Allemani C, Tereanu C, De Angelis R, Capocaccia R, Visser O, Marcos-Gragera R, Maynadié M, Simonetti A, Lutz JM, Berrino F, Hackl M, Holub J, Maynadie M, Holleczek B, Tryggvadottir L, Comber H, Bellù F, Giacomin A, Ferretti S, Crocetti E, Serraino D, Vercelli M, Federico M, Fusco M, Michiara M, Tumino R, Mangone L, Falcini F, Iannelli A, Budroni M, Zanetti R, Piffer S, La Rosa F, Zambon P, Sowe S, England K, Langmark F, Rachtan J, Mezyk R, Zwierko M, Ondrusova M, Primic-Žakelj M, Khan S, Jundt G, Usel M, Ess SM, Bordoni A, Otter R, Coebergh JW, Siesling S, Greenberg D, Easey N, Roche M, Lawrence G, Gavin A, Brewster DH, Steward J (2010) Incidence of hematologic malignancies in Europe by morphologic subtype: Results of the HAEMACARE project. Blood 116(19): 3724–3734 [DOI] [PubMed] [Google Scholar]
  33. Smedby KE, Askling J, Mariette X, Baecklund E (2008) Autoimmune and inflammatory disorders and risk of malignant lymphomas--an update. J Intern Med 264(6): 514–527 [DOI] [PubMed] [Google Scholar]
  34. Smith-Warner SA, Spiegelman D, Ritz J, Albanes D, Beeson WL, Bernstein L, Berrino F, van den Brandt PA, Buring JE, Cho E, Colditz GA, Folsom AR, Freudenheim JL, Giovannucci E, Goldbohm RA, Graham S, Harnack L, Horn-Ross PL, Krogh V, Leitzmann MF, McCullough ML, Miller AB, Rodriguez C, Rohan TE, Schatzkin A, Shore R, Virtanen M, Willett WC, Wolk A, Zeleniuch-Jacquotte A, Zhang SM, Hunter DJ (2006) Methods for pooling results of epidemiologic studies: the Pooling Project of Prospective Studies of Diet and Cancer. Am J Epidemiol 163(11): 1053–1064 [DOI] [PubMed] [Google Scholar]
  35. StataCorp (2009) Stata Statistical Software. Release 11
  36. Strom SS, Gu Y, Gruschkus SK, Pierce SA, Estey EH (2005) Risk factors of myelodysplastic syndromes: A case-control study. Leukemia 19(11): 1912–1918 [DOI] [PubMed] [Google Scholar]
  37. Swerdlow SCE, Harris N (2008) World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues. IARC Press: Lyon, France [Google Scholar]
  38. Townsend PPP, Beattie A (1988) Health and deprivation: inequality and the North. Croom Helm: London [Google Scholar]
  39. Troy JD, Hartge P, Weissfeld JL, Oken MM, Colditz GA, Mechanic LE, Morton LM (2010) Associations between anthropometry, cigarette smoking, alcohol consumption, and non-Hodgkin lymphoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Am J Epidemiol 171(12): 1270–1281 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Turner JJ, Morton LM, Linet MS, Clarke CA, Kadin ME, Vajdic CM, Monnereau A, Maynadié M, Chiu BCH, Marcos-Gragera R, Costantini AS, Cerhan JR, Weisenburger DD (2010) InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): Update and future directions. Blood 116(20): e90–e98 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from British Journal of Cancer are provided here courtesy of Cancer Research UK

RESOURCES