American Journal of Epidemiology
ª The Author 2010. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: [email protected].
Vol. 172, No. 6
DOI: 10.1093/aje/kwq167
Advance Access publication:
August 18, 2010
Systematic Reviews and Meta- and Pooled Analyses
Birth Order and Risk of Non-Hodgkin Lymphoma—True Association or Bias?
Andrew E. Grulich*, Claire M. Vajdic, Michael O. Falster, Eleanor Kane, Karin Ekstrom Smedby,
Paige M. Bracci, Silvia de Sanjose, Nikolaus Becker, Jenny Turner, Otoniel Martinez-Maza,
Mads Melbye, Eric A. Engels, Paolo Vineis, Adele Seniori Costantini, Elizabeth A. Holly, John
J. Spinelli, Carlo La Vecchia, Tongzhang Zheng, Brian C. H. Chiu, Silvia Franceschi,
Pierluigi Cocco, Marc Maynadié, Lenka Foretova, Anthony Staines, Paul Brennan, Scott Davis,
Richard K. Severson, James R. Cerhan, Elizabeth C. Breen, Brenda Birmann, and Wendy Cozen
* Correspondence to Prof. Andrew E. Grulich, HIV Epidemiology and Prevention Program, National Centre in HIV Epidemiology and
Clinical Research, University of New South Wales, Sydney, NSW 2052, Australia (e-mail: [email protected]).
Initially submitted February 11, 2010; accepted for publication May 10, 2010.
There is inconsistent evidence that increasing birth order may be associated with risk of non-Hodgkin lymphoma
(NHL). The authors examined the association between birth order and related variables and NHL risk in a pooled
analysis (1983–2005) of 13,535 cases and 16,427 controls from 18 case-control studies within the International
Lymphoma Epidemiology Consortium (InterLymph). Overall, the authors found no significant association between
increasing birth order and risk of NHL (P-trend ¼ 0.082) and significant heterogeneity. However, a significant
association was present for a number of B- and T-cell NHL subtypes. There was considerable variation in the
study-specific risks which was partly explained by study design and participant characteristics. In particular,
a significant positive association was present in population-based studies, which had lower response rates in
cases and controls, but not in hospital-based studies. A significant positive association was present in highersocioeconomic-status (SES) participants only. Results were very similar for the related variable of sibship size. The
known correlation of high birth order with low SES suggests that selection bias related to SES may be responsible
for the association between birth order and NHL.
birth order; case-control studies; lymphoma, non-Hodgkin; selection bias; social class
Abbreviations: NHL, non-Hodgkin lymphoma; SES, socioeconomic status.
Non-Hodgkin lymphoma (NHL) is a common cancer in
industrialized nations, and incidence has increased dramatically over the last 50 years (1). Lymphomas arise from
cells of the human immune system, which is known to be
critically shaped by early-life environmental exposures, including infections. Infection very early in life may fundamentally alter immune function and in this way may
influence lymphoma risk (1). Increasing birth order has been
widely studied as a proxy for increased likelihood of earlylife exposure to infection and as a risk factor for lymphoma.
Although an inverse relation between increasing birth order
and risk of Hodgkin lymphoma has been reported in most
case-control (2) and cohort (3) studies, results from studies
of NHL have been inconsistent. Recently, investigators in
several case-control studies reported a positive relation between increasing birth order and risk of NHL (4–7), but
others have found no association (8–12). In addition, researchers in other case-control studies have reported that
NHL risk increases with sibship size (13), while others have
reported no association (8, 11, 14). In no studies have investigators reported a significant inverse association. Thus,
there are inconsistent data from case-control studies that
increasing birth order may be associated with increasing
NHL risk (15). Until recently, cohort studies have been
too small to provide informative data on the association
between birth order and NHL incidence. In 2006, in a
large-scale population-based Swedish cohort study of over
7,000 cases of incident NHL, Altieri et al. (3) reported no
621
Am J Epidemiol 2010;172:621–630
Cases
First Author, Year
(Reference No.)
Location of Study
Years
of Study
Age Range,
years
Matching Variables
No.
Controls
Participation
Rate, %
No.
Participation
Rate, %
Source of
Controls
Talamini, 2004 (28)
Aviano and Napoli, Italy
1999–2002
18–84
None
225
97
504
91
Hospital patients
Spinelli, 2007 (21)
British Columbia, Canada
2000–2003
20–79
Age, sex, and region
828
85
845
50
Random selection
from client registry
of Ministry of Health
Zhang, 2005 (29)
Connecticut, United States
1995–2001
21–84
Age
597
72
716
47–69
<65 years: RDD; 65
years: random
selection from CMS
EpiLymph (multicenter
case-control study
of lymphoma)
Europe
de Sanjose, 2004 (22)
Spain
1998–2003
17–96
Age, sex, and region
435
82
630
96
Hospital patients
Becker, 2004 (10)
Germany
1999–2002
18–82
Age, sex, and region
518
87
518
44
Random selection
from population
registries
Besson, 2006 (23)
Ireland
1998–2004
19–85
Age, sex, and center
144
90
208
75
Hospital patients
Becker, 2007 (12)
Czech Republic
2001–2003
19–82
Age, sex, and region
199
90
199
60
Hospital patients
Becker, 2007 (12)
France
2000–2003
18–82
Age, sex, and region
217
91
272
74
Hospital patients
Cocco, 2008 (24)
Italy (Sardinia)
1998–2004
25–81
Age, sex, and region
219
93
336
66
Random selection
from population
registries
Am J Epidemiol 2010;172:621–630
Vineis, 2000 (14)
11 cities in Italy
1990–1993
20–74
Age, sex, and region
1,640
82
1,771
74
Random selection
from demographic
or National Health
Service files
Cerhan, 2007 (25)
(Mayo Clinic)
Minnesota, Iowa, and
Wisconsin, United States
2002–2005
20–87
Age, sex, and region
500
65
499
69
Patients attending a
prescheduled
general medical
examination
Cozen, 2007 (6)
(NCI-SEER)
Detroit, Michigan; Iowa;
Los Angeles, California;
and Seattle, Washington,
United States
1998–2001
20–74
Age, sex, region,
and race/ethnicity
1,316
76
1,055
52
<65 years: RDD;
65 years: random
selection from CMS
Chiu, 2005 (30)
Nebraska, United States
1999–2002
20–75
Age and sex
386
74
535
78
Tavani, 2000 (26)
Aviano and Milan, Italy
1983–1992
17–85
None
429
>97
1,157
>97
Grulich, 2005 (4)
New South Wales and
Australian Capital
Territory, Australia
2000–2001
20–74
Age, sex, and region
694
85
694
61
Random selection
from electoral rolls
Smedby, 2007
(5) (SCALE)
Denmark and Sweden
1999–2002
18–74
Age, sex, and country
3,055
81
3,187
71
Random selection
from population
registries
Bracci, 2006 (7)
San Francisco, California,
United States
1988–1993
21–74
Age, sex, and region
1,305
72
2,404
78
<65 years: RDD;
65 years:
random selection
from CMS
(population-based)
Willett, 2005 (27)
Parts of northern and
southwestern England,
United Kingdom
1998–2003
16–69
Age, sex, and region
828
75
897
71
Random selection
from general
practice lists
RDD
Hospital patients
Abbreviations: CMS, Centers for Medicare and Medicaid Services; NCI, National Cancer Institute; RDD, random digit dialing; SCALE, Scandinavian Lymphoma Epidemiology Study; SEER,
Surveillance, Epidemiology, and End Results [Program].
622 Grulich et al.
Table 1. Characteristics of Participants From 18 Non-Hodgkin Lymphoma Case-Control Studies Included in a Pooled Analysis of Birth Order/Sibship Size and Risk of Non-Hodgkin
Lymphoma, 1983–2005
Birth Order and Risk of Non-Hodgkin Lymphoma
Table 2. Demographic Characteristics of Cases and Controls From
18 Non-Hodgkin Lymphoma Case-Control Studies Included in
a Pooled Analysis of Birth Order/Sibship Size and Risk of NonHodgkin Lymphoma, 1983–2005
Cases
Controls
Demographic Factor
No.
Pooled total
%
13,535
No.
%
16,427
623
96 years); collection of data on birth order or sibship size;
and the availability of an electronic data set in March 2007.
Organ transplant recipients and persons with human immunodeficiency virus infection were excluded. Because of variations in study design among the 6 EpiLymph study centers
(Table 1), we treated this study statistically as 6 separate
studies, to allow for assessment of center-specific effects
and heterogeneity.
Sex
Male
7,329
54.1
8,865
54.0
Female
6,206
45.9
7,562
46.0
25
0.2
56
0.3
20–29
381
2.8
842
5.1
30–39
924
6.8
1,602
9.8
40–49
1,823
13.5
2,386
14.5
50–59
3,472
25.7
3,729
22.7
60–69
4,313
31.9
4,759
29.0
70–79
2,448
18.1
2,885
17.6
147
1.1
168
1.0
Age, yearsa
<20
80
Education/SES (tertile)
Low
5,205
38.5
5,802
35.3
Medium
4,643
34.3
5,883
35.8
High
3,577
26.4
4,642
28.2
110
0.8
100
0.6
5,672
41.9
6,757
41.1
Unknown/other
Race/ethnicity
White
Black
202
1.5
327
2.0
Other/mixed
539
4.0
558
3.4
Missing data
7,122
52.6
8,785
53.5
Abbreviation: SES, socioeconomic status.
The median age of cases was 58 years (range, 16–96); the median age of controls was 60 years (range, 17–89). Data on age were
missing for 2 cases.
a
association between NHL and birth order (P-trend ¼ 0.464).
However, this was a study of predominantly young persons,
with a mean age of NHL occurrence of 40 years (3).
Recently, it has been hypothesized that selection bias
may explain the inconsistency in the results of case-control
studies of birth order and NHL risk (11). To further investigate the association between birth order and related variables indicative of childhood crowding and NHL risk, we
conducted a pooled analysis of case-control studies from
the International Lymphoma Epidemiology Consortium
(InterLymph).
MATERIALS AND METHODS
We performed a pooled analysis of data from 18 casecontrol studies included in InterLymph (www.epi.grants.
cancer.gov/InterLymph). Participating studies (Table 1)
met the following eligibility criteria: cases diagnosed with
incident, histologically confirmed NHL as adults (ages 16–
Am J Epidemiol 2010;172:621–630
Exposure assessment
Investigators in all studies collected self-reported data on
birth order and/or sibship size. Age differences with the
nearest older and younger siblings were collected in 10
and 9 studies, respectively. For the birth-order analyses,
the referent category was firstborn children, including only
children. Analyses were also performed after separating
only children from the referent category. Because results
were very similar, they are not presented here (data not
shown). Socioeconomic status (SES) groups in each study
were based on the tertile distribution of years of education
(11 studies) or area-based deprivation levels obtained from
census data (11) (2 studies) in the controls within each individual study.
Statistical methods
Odds ratios and 95% confidence intervals were computed from unconditional logistic regression models, using
a 2-stage random-effects model to estimate relative risk
(hereafter called ‘‘risk’’) of NHL and a joint fixed-effects
model to estimate risk by NHL subtype (16). All World
Health Organization classification (17) subtypes of NHL
except multiple myeloma were included in the analysis, as
recommended for epidemiologic analyses (18). Results in
all models were adjusted for the matching variables age,
sex, and region/study center. Tests for a linear trend in
odds ratios were performed using a generalized leastsquares trend estimation procedure that assumed a linear
relation in exposure for increasing birth order and sibship
size (19).
Heterogeneity among study centers was assessed using
Cochran’s Q statistic and the I2 statistic (20). In the presence of significant heterogeneity (P < 0.10), forest plots were
used to identify outlying studies, and sensitivity analyses
were performed with and without the outlying studies; no
individual study was consistently identified as outlying. Because of a predominance of study participants of Caucasian
origin, stratification by race/ethnicity was not meaningful.
Restriction of analyses to Caucasians gave results similar to
those from analyses that included all participants. All statistical tests were 2-sided and assumed an a error level of 0.05.
Analyses were performed using STATA software, version
10.0 (Stata Corporation, College Station, Texas).
To examine the possible effect of bias, we conducted
sensitivity analyses by stratifying by study design factors,
including published response rates in cases and controls
(tertiles) and source of controls (population vs. hospital).
In addition, results were stratified by sex and SES.
624 Grulich et al.
Table 3. Odds Ratios for Non-Hodgkin Lymphoma According to Birth Order/Sibship Size in
a Pooled Analysis of Results From 18 Non-Hodgkin Lymphoma Case-Control Studies Within the
International Lymphoma Epidemiology Consortium (InterLymph), 1983–2005
Heterogeneity
Familial
Structure Variable
No. of
Cases
No. of
Controls
Odds
Ratioa
95% Confidence
Interval
No. of
Studies
P Value
I 2, %
Birth order
Firstborn
3,610
4,579
1.00
Second-born
2,595
3,311
1.01
0.92, 1.10
14
0.19
24.5
Third-born
1,520
1,841
1.05
0.93, 1.20
14
0.02
48.1
816
977
1.12
0.96, 1.30
14
0.05
42.0
1,228
1,401
1.12
0.95, 1.32
14
<0.01
60.9
0.0
Fourth-born
Fifth-born or higher
P-trend
0.082
Sibship size
Only child
1,283
1,581
1.00
1
2,820
3,591
1.02
0.93, 1.12
18
0.83
2
2,668
3,290
1.06
0.96, 1.16
18
0.72
0.0
3
1,902
2,314
1.04
0.88, 1.21
18
0.02
46.7
4
1,248
1,591
1.01
0.87, 1.18
18
0.09
32.8
5
2,720
3,235
1.10
0.93, 1.29
18
0.01
50.9
No. of other siblings
P-trend
0.313
a
Odds ratios and 95% confidence intervals were computed using a 2-stage random-effects
model, adjusted for age (in 5-year intervals), sex, and study center.
The pooled analyses were approved by the University of
New South Wales Human Research Ethics Committee.
RESULTS
This analysis included data on 13,535 cases and 16,427
controls. Data on both birth order and sibship size were
collected in 14 studies (4–6, 10, 12, 21–27); in 4 additional
studies (14, 28–30), data were available on sibship size only
(Table 1). Demographic characteristics of participants are
summarized in Table 2.
Overall, there was no significant association between increasing birth order and increasing risk of NHL (Table 3).
Among participants whose birth order was fifth or higher,
the pooled odds ratio was 1.12 (95% confidence interval:
0.95, 1.32) as compared with those who were firstborn, and
the test for trend with increasing birth order was not significant (P-trend ¼ 0.082). The direction of the association
was the same for sibship size and was also nonsignificant
(P-trend ¼ 0.313). Within individual strata of sibship size,
there was no consistent association of NHL risk with increasing birth order (data not shown). Study-specific odds
ratios for all birth orders and most sibship sizes were heterogeneous, with the greatest variation being seen at higher
birth orders and higher sibship sizes (see Web Figure 1
(http://aje.oxfordjournals.org/) and Table 3). A statistically
significant positive association with birth order and sibship
size was present in Australian and North American studies,
but in Europe an association was present only in the Scandinavian study (5). All of these studies were population-based.
Pooled odds ratios varied by case and control response
rate and by study design. Positive associations with birth
order and sibship size of 5 or more were confined to studies
in the lower 2 tertiles of case response rate (Table 4). In
contrast, among studies in the top tertile (90%–97%), the
directions of the associations were reversed, in that cases
were more likely rather than less likely to be firstborn and
were less likely to come from large sibships. Similarly, the
positive associations in the highest category of birth order
and sibship size were confined to studies with the lowest
control response rate tertile (Table 5). Reported response
rates tended to be lower in population-based studies than
in hospital-based studies, and positive trends with birth order and sibship size occurred in population-based studies
(birth order: P-trend < 0.001; sibship size: P-trend < 0.001)
but not in hospital-based studies (birth order: P-trend ¼ 0.391;
sibship size: P-trend ¼ 0.118). Because of this overlap, it
was impossible to separate the impacts of study design and
response rates on the study-specific odds ratios.
There was also variation in the pooled odds ratios when
data were examined according to the demographic features
of the respondents. When results were stratified by SES, the
positive trend with birth order was not found in the lowest
tertile, was of borderline significance in the middle tertile,
and was significant in the top tertile (Table 6). For increasing
sibship size, a positive trend was also present only in the
top tertile of SES. Positive trends were closer to statistical
significance among women (birth order: P-trend ¼ 0.008,
sibship size: P-trend ¼ 0.052) than among men (birth order:
P-trend ¼ 0.088; sibship size: P-trend ¼ 0.167).
Am J Epidemiol 2010;172:621–630
Birth Order and Risk of Non-Hodgkin Lymphoma
625
Table 4. Odds Ratios for Non-Hodgkin Lymphoma According to Birth Order/Sibship Size, by Response Rate Among Cases, in a Pooled Analysis
of Results From 18 Non-Hodgkin Lymphoma Case-Control Studies Within the International Lymphoma Epidemiology Consortium (InterLymph),
1983–2005
Tertile of Response Rate in Cases (Range)
Familial
Structure Variable
Low (65%–76%)
No. of
Cases
No. of
Controls
ORa
Medium (81%–87%)
95% CI
No. of
Cases
No. of
Controls
ORa
High (90%–97%)
95% CI
No. of
Cases
No. of
Controls
ORa
95% CI
Birth order
Firstborn
1,196
1,641
1.00
2,300
2,001
1.00
413
638
1.00
Second-born
838
1,156
1.02
0.91, 1.15
1,615
1,469
1.05
0.90, 1.24
288
540
0.84
0.68, 1.04
Third-born
474
634
1.09
0.94, 1.26
853
872
1.20
1.02, 1.40
174
354
0.75
0.54, 1.05
Fourth-born
275
318
1.29
1.07, 1.55
452
431
1.18
0.89, 1.57
110
207
0.78
0.59, 1.03
Fifth-born or higher
388
423
1.34
1.14, 1.58
569
635
1.26
0.97, 1.63
205
409
0.77
0.61, 0.96
P-trend
<0.001
No. of studies
4
0.011
0.018
5
5
Sibship size
Only child
358
484
1.00
828
974
1.00
1
928
1,288
1.03
2
930
1,207
1.11
3
636
877
4
450
5
960
97
123
1.00
0.87, 1.21
1,624
1,858
1.05
0.94, 1.31
1,473
1,612
1.08
0.93, 1.19
268
445
0.78
0.53, 1.15
0.96, 1.23
265
471
0.75
1.07
0.90, 1.28
1,072
1,039
1.23
0.54, 1.04
1.07, 1.40
194
398
0.62
0.36, 1.06
592
1.14
0.94, 1.38
651
690
1.12
0.96, 1.30
147
309
0.61
0.36, 1.04
1,069
1.31
1.11, 1.56
1,321
1,270
1.21
1.02, 1.44
439
896
0.58
0.41, 0.82
No. of other siblings
P-trend
No. of studies
<0.001
6
0.004
0.001
6
6
Abbreviations: CI, confidence interval; OR, odds ratio.
Odds ratios and 95% confidence intervals were computed using a 2-stage random-effects model, adjusted for age (in 5-year intervals), sex,
and study center.
a
The associations with birth order and sibship size were
not specific to any particular NHL subtype. Statistically
significant positive trends with increasing birth order were
found for B-cell NHL overall (P-trend < 0.001) and for
follicular NHL (P-trend ¼ 0.001), diffuse large B-cell NHL
(P-trend ¼ 0.002), and precursor B-cell NHL (P-trend ¼
0.025). Trends were also significant for T-cell NHL
(P-trend ¼ 0.002) and for the mycosis fungoides subtype
(P-trend ¼ 0.039). For increasing sibship size, significant
positive trends were seen for B-cell NHL (P-trend < 0.001),
follicular NHL (P-trend ¼ 0.012), and B-cell lymphoma not
otherwise specified (P-trend ¼ 0.004) and were of borderline
significance for diffuse large-cell NHL (P-trend ¼ 0.062).
However, the majority of lymphoma subtypes, including the
category of NHL not otherwise specified, were not associated
with either birth order or sibship size.
There was no association with number of younger siblings, age difference with the nearest older or younger sibling, or sharing a bed or bedroom as a child (data not shown).
DISCUSSION
In this large pooled analysis of data from case-control
studies, we found no significant association between birth
order and sibship size and risk of NHL overall. However,
significant positive associations were present for a number
Am J Epidemiol 2010;172:621–630
of B- and T-cell lymphoma subtypes. Substantial heterogeneity in study-specific risks was observed, which may reflect
variations in the association by response rate, study population, and/or SES. The positive associations were confined
to population-based studies, which had lower response rates
in cases and controls, and were absent in hospital-based
studies, which had higher response rates. After stratifying
results by SES, the positive associations were present
among persons of upper SES but not those of lower SES.
The fact that the birth order association was confined to
studies with lower response rates and to upper-SES participants and was not specific to particular B- or T-cell subtypes suggests that the previously reported positive
association of NHL risk with birth order and sibship size
in case-control studies may have been due to selection bias
mediated by SES.
Selection bias may have arisen because of differential
response rates by SES. In almost all developed countries,
lower SES is strongly associated with a higher total fertility
rate, and therefore with birth order of the offspring (31–33).
Although we did not have data on parental SES, the SES of
parents correlates strongly with that of their children (34), so
participant SES is likely to be correlated with the participant’s own birth order. Among controls in this study, increasing participant SES was strongly inversely correlated
with the participant’s own birth order (P < 0.001). Low
626 Grulich et al.
Table 5. Odds Ratios for Non-Hodgkin Lymphoma According to Birth Order/Sibship Size, by Response Rate Among Controls, in a Pooled
Analysis of Results From 18 Non-Hodgkin Lymphoma Case-Control Studies Within the International Lymphoma Epidemiology Consortium
(InterLymph), 1983–2005
Tertile of Response Rate in Controls (Range)
Familial
Structure Variable
Low (44%–61%)
No. of
Cases
No. of
Controls
ORa
Medium (66%–74%)
95% CI
No. of
Cases
No. of
Controls
ORa
High (75%–99%)
95% CI
No. of
Cases
No. of
Controls
ORa
95% CI
Birth order
Firstborn
1,047
1,106
1.00
1,794
2,002
1.00
769
1,471
1.00
Second-born
746
760
1.02
0.82, 1.26
1,282
1,430
1.01
0.89, 1.14
567
1,121
1.00
0.87, 1.14
Third-born
435
380
1.27
1.08, 1.50
730
751
1.00
0.78, 1.27
355
710
0.89
0.69, 1.15
Fourth-born
240
183
1.45
1.17, 1.80
371
400
1.06
0.83, 1.36
205
394
0.88
0.64, 1.22
Fifth-born or higher
267
215
1.34
1.00, 1.79
572
528
1.08
0.79, 1.48
389
658
0.96
0.70, 1.31
P-trend
<0.001
No. of studies
5
0.577
0.511
5
4
Sibship size
Only child
299
337
1.00
797
926
1.00
1
836
890
1.05
2
788
774
1.14
3
565
534
4
342
5
641
187
318
1.00
0.87, 1.27
1,442
1,651
1.03
0.95, 1.38
1,319
1,445
1.06
0.89, 1.18
542
1,050
0.94
0.76, 1.16
0.93, 1.20
561
1,071
0.93
1.20
0.98, 1.47
915
910
1.13
0.73, 1.19
0.92, 1.38
422
870
0.72
0.46, 1.13
360
1.06
0.85, 1.32
595
586
1.15
0.93, 1.42
311
645
0.73
0.49, 1.09
573
1.28
1.04, 1.56
1,210
1,234
1.06
0.81, 1.40
869
1,428
0.85
0.54, 1.33
No. of other siblings
P-trend
0.012
0.206
0.351
No. of studies
6
6
6
Abbreviations: CI, confidence interval; OR, odds ratio.
Odds ratios and 95% confidence intervals were computed using a 2-stage random-effects model, adjusted for age (in 5-year intervals),
sex, and study center.
a
response rates increase the probability of selection bias.
Thus, our finding that the positive association with birth
order was confined to those studies with lower response
rates is consistent with the association’s being due to selection bias. However, low response rates do not in themselves
cause selection bias (35). Selection bias arises only if participation differs between exposed (or nonexposed) cases
and exposed (or nonexposed) controls. A SES-related selection bias leading to a positive association between NHL risk
and birth order can arise in 2 main ways. First, among controls, it can arise if the participation rate is higher among
persons with a low birth order. Second, among cases, it can
arise if the participation rate is higher among persons of high
birth order. Because SES and birth order are correlated, this
equates to a higher participation rate among high-SES controls than among low-SES controls or a higher participation
rate among low-SES cases than among high-SES cases.
There is consistent evidence that in population-based
case-control studies, the response rate is higher in highSES controls than in low-SES controls. This has been shown
for controls recruited through a variety of methods, including random digit dialing (36–38), contact with their general
practitioners (39, 40), and letter or telephone after random
selection from health insurance (41), electoral (42), or census (43) rolls. This selection bias would tend to generate
a positive association between birth order and disease risk.
However, this does not explain why the effect of birth order
was most prominent in the high-SES stratum (Table 6), in
which response rates are likely to be higher.
Since response rates are generally substantially lower in
controls than in cases in population-based studies, there is
less potential for selection bias to arise from factors associated with case selection. In case-control studies of cancer,
the reasons for case nonparticipation are clearly different
from the reasons for control nonparticipation. The most
common reasons include having died of cancer prior to interview or being too ill to participate (11). In a populationbased Italian case-control study of cancer, Richiardi et al.
(43) reported that nonparticipating cases were of high SES
rather than low SES, and they hypothesized that this may
have been because more educated and wealthy subjects retained more independence when hospitalized for a serious
disease. However, other cancer case-control studies have
identified a pattern similar to that of controls, with higher
participation among cases of higher SES (38, 44, 45).
In addition to selection bias, there is at least 1 alternative
explanation for the lack of an association between birth
order and NHL risk in studies with the highest response rates
included in our analysis. Most (but not all) of the studies
with high response rates, particularly among controls, were
hospital-based case-control studies. Even when response
rates are high, studies of this design are prone to different
Am J Epidemiol 2010;172:621–630
Birth Order and Risk of Non-Hodgkin Lymphoma
627
Table 6. Odds Ratios for Non-Hodgkin Lymphoma According to Birth Order/Sibship Size, by Socioeconomic Status of Participants, in a Pooled
Analysis of Results From 18 Non-Hodgkin Lymphoma Case-Control Studies Within the International Lymphoma Epidemiology Consortium
(InterLymph), 1983–2005
Tertile of Participant Socioeconomic Statusa
Familial
Structure Variable
Low
No. of
Cases
No. of
Controls
ORb
Medium
95% CIb
No. of
Cases
No. of
Controls
OR
High
95% CI
No. of
Cases
No. of
Controls
OR
95% CI
Birth order
Firstborn
1,151
1,262
1.00
1,292
1,754
1.00
1,138
1,525
1.00
Second-born
885
1,001
1.00
0.86, 1.17
934
1,273
1.02
0.91, 1.15
755
1,009
1.06
0.92, 1.23
Third-born
573
670
0.93
0.77, 1.14
544
662
1.21
1.00, 1.46
385
496
1.11
0.93, 1.32
Fourth-born
345
389
1.03
0.87, 1.23
288
362
1.24
0.98, 1.58
180
225
1.14
0.86, 1.49
Fifth-born or higher
608
654
1.07
0.87, 1.32
377
516
1.11
0.90, 1.37
236
225
1.49
1.14, 1.95
P-trend
0.548
0.067
0.007
Sibship size
Only child
439
436
1.00
408
575
1.00
432
561
1.00
No. of other siblings
1
884
978
0.91
0.77, 1.08
1,005
1,306
1.15
0.97, 1.35
910
1,283
1.00
0.84, 1.18
2
915
1,038
0.91
0.77, 1.08
935
1,178
1.17
0.98, 1.39
797
1,058
1.11
0.93, 1.32
3
751
814
0.99
0.83, 1.18
655
848
1.15
0.88, 1.51
482
637
1.06
0.87, 1.30
4
563
685
0.89
0.73, 1.07
404
524
1.14
0.86, 1.52
273
368
1.08
0.86, 1.37
1,521
1,705
1.03
0.87, 1.22
768
1,054
1.05
0.78, 1.40
416
467
1.31
1.06, 1.62
5
P-trend
0.146
0.803
0.008
Abbreviations: CI, confidence interval; OR, odds ratio.
Socioeconomic status groups in each study were based on the tertile distribution of years of education (11 studies) or area-based deprivation
levels obtained from census data (11).
b
Odds ratios and 95% confidence intervals were computed using a 2-stage random-effects model, with adjustment for age (in 5-year intervals),
sex, and study center.
a
selection biases (46). For example, if all patients with NHL
are treated in a hospital but low SES predicts hospital attendance for other common conditions such as trauma or cardiovascular disease, then low-SES (high birth order) people
may be overrepresented among hospital-based controls.
Among hospital-based studies in our data set, birth order
was not associated with NHL risk. Among population-based
studies, which are generally regarded as methodologically
superior to hospital-based studies, there was a significant
association. However, because these were also the studies
with the lowest response rates, it was not possible to conclude that this positive association was likely to reflect
a causal association. Even among population-based studies,
the positive association between birth order and NHL varied
by case and control response rates (data not shown).
If the birth order association is due to selection bias,
a similar effect is likely to occur in case-control studies of
birth order as a risk factor for other diseases. Birth order has
been studied in some detail as a risk factor for Hodgkin
lymphoma. Investigators in population-based cohort studies
(3, 47) and case-control studies with low refusal rates (9, 48–
50) are consistent in reporting that increasing birth order
and/or sibship size is associated with decreasing Hodgkin
lymphoma risk, especially the young adult type. However, in
some recent case-control studies which had lower response
rates in controls, researchers reported no association with
low birth order or other markers of higher SES (51–53). One
Am J Epidemiol 2010;172:621–630
possibility for the lack of association is a temporal change in
early-life child-care arrangements, resulting in increased
early-life exposure to infection that mutes the effect of birth
order. Alternatively, in 1 of these studies, Glaser et al. (54)
reported that the absence of an effect was probably related
to a combination of the low response rate in controls and
the fact that controls who participated were of higher SES
than those who did not participate. This is consistent with
the form and direction of selection bias which may have
occurred in NHL research: The difference is that for Hodgkin lymphoma, selection bias has sometimes obscured
a true inverse relation with birth order, whereas for NHL
it may have created the impression of a positive relation.
For other types of cancer, birth order has been less intensively studied, although patterns of increased and decreased risk have been described for certain cancer sites
in cohort studies, perhaps reflecting the effect of SES on
cancer risk (55).
In case-control studies that have observed an association
of NHL risk with increasing birth order, researchers have
interpreted the finding as suggesting that the immunologic
consequences of early infection may be responsible for increased NHL risk in persons of late birth order (4, 6, 7). Our
finding of no significant association between NHL risk and
birth order does not rule out an effect of early life environment, but it does suggest that such an effect, if present, is
unlikely to be reliably mediated by birth order.
628 Grulich et al.
The large size of this pooled analysis using individual
data allowed stratification by study design and participant
demographic characteristics, thus enabling exploration of
factors related to the weak association observed for some
NHL subtypes. In previous InterLymph pooled analyses we
have carried out, for atopic diseases and for autoimmune
disorders, there was very little difference in the pattern or
significance of results when we stratified results by these
design features or demographic variables (56, 57). A possible reason for the heterogeneity of the findings we have
demonstrated for birth order is that the infection-related
consequences of birth order are highly variable among the
countries in which these studies were conducted. In terms of
selection bias, unfortunately investigators in most of the
participating studies did not collect data on nonresponders
to allow direct investigation of the hypothesis presented
here. However, in 1 population-based study, Mensah et al.
(11) investigated nonresponse and reported that both the
participating cases and controls were of higher-than-expected
SES. Shen et al. (37) reported varying results on participation rate by SES according to individual study center. When
response rates are substantially lower in controls than in
cases, higher participation rates in persons of higher SES
will lead to selection bias, causing an association between
increasing birth order and NHL risk, as we have hypothesized may be the case. Because of variations in how investigators in participating studies reported nonresponse, it is
likely that there was some misclassification of true nonresponse rates. The absence of universally accepted criteria
for reporting response rates (58) and a lack of consistency
between studies in recording of details of nonresponse precluded any recalculation of standardized response rates in
this pooled analysis.
In summary, we did not find a significant overall association between birth order or sibship size and risk of
NHL in this large pooled analysis. However, a positive
association was found in population-based studies and
among people in the highest SES stratum. Sensitivity
analyses by study design factors and participant characteristics, a strength of pooled analyses, suggested that
a likely explanation for the weak positive association
between birth order and NHL risk reported in some
case-control studies may be selection bias. The fact that
low-SES controls in this pooled analysis were more
likely to be of high birth order and were less likely to
participate may have generated a weak positive association between NHL risk and birth order. More generally,
our results show that because of the close association
between birth order and SES and between SES and subject nonresponse, case-control studies of birth order as
a risk factor for disease will only be valid if the response
rate is very high in both cases and controls and if other
sources of selection bias related to SES are eliminated.
Confounding or selection bias due to SES should be
ruled out in all studies which have identified birth order
as a risk factor for disease. The association is best explored in population-based case-control studies with
high response rates, or in large prospective or retrospective cohort studies that utilize linked cancer and birth
registry records.
ACKNOWLEDGMENTS
Author affiliations: National Centre in HIV Epidemiology
and Clinical Research, University of New South Wales,
Sydney, New South Wales, Australia (Andrew E. Grulich);
UNSW Cancer Research Centre, Prince of Wales Clinical
School, University of New South Wales, Sydney, New South
Wales, Australia (Claire M. Vajdic, Michael O. Falster);
UCLA AIDS Institute and Jonsson Comprehensive Cancer
Center, David Geffen School of Medicine, University of
California, Los Angeles, Los Angeles, California (Otoniel
Martinez-Maza); Epidemiologia i Registre del Càncer, Institut Català d’Oncologia, Barcelona, Spain (Silvia de Sanjose);
El Centro de Investigación Biomédica en Red de Epidemiologı́a y Salud Pública (CIBERESP), Barcelona, Spain (Silvia
de Sanjose); Division of Clinical Epidemiology, German
Cancer Research Centre, Heidelberg, Germany (Nikolaus
Becker); Department of Epidemiology and Biostatistics,
School of Medicine, University of California, San Francisco,
San Francisco, California (Paige M. Bracci, Elizabeth A.
Holly); Department of Anatomical Pathology, St. Vincent’s
Hospital, Sydney, New South Wales, Australia (Jenny
Turner); Epidemiology and Genetics Unit, Department of
Health Sciences, University of York, York, United Kingdom
(Eleanor Kane); Department of Medicine, Clinical Epidemiology Unit, Karolinska Institute, Stockholm, Sweden (Karin
Ekstrom Smedby); Department of Medical Epidemiology
and Biostatistics, Karolinska Institute, Stockholm, Sweden
(Karin Ekstrom Smedby); Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark (Mads
Melbye); Infections and Immunoepidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland (Eric A. Engels); Division
of Epidemiology, Public Health and Primary Care, Faculty
of Medicine, Imperial College London, London, United
Kingdom (Paolo Vineis); Occupational and Environmental
Epidemiology Unit, Cancer Prevention and Research Institute, Florence, Italy (Adele Seniori Costantini); Cancer Control Research Program, British Columbia Cancer Agency,
Vancouver, British Columbia, Canada (John J. Spinelli); Istituto di Ricerche Farmacologiche ‘‘Mario Negri’’ and Istituto
di Statistica Medica e Biometria, Università degli Studi di
Milano, Milan, Italy (Carlo La Vecchia); Department of
Epidemiology and Public Health, Yale School of Medicine,
New Haven, Connecticut (Tongzhang Zheng); Department of
Health Studies, Division of Biological Sciences, University
of Chicago, Chicago, Illinois (Brian C. H. Chiu); International Agency for Research on Cancer, Lyon, France (Silvia
Franceschi, Paul Brennan); Department of Public Health,
Occupational Health Section, University of Cagliari, Cagliari,
Italy (Pierluigi Cocco); Registry of Hematological Malignancies of Cote d’Or, Dijon University Hospital, Dijon, France
(Marc Maynadié); Department of Cancer Epidemiology and
Genetics, Masaryk Memorial Cancer Institute, Brno, Czech
Republic (Lenka Foretova); School of Nursing, Dublin City
University, Dublin, Ireland (Anthony Staines); Fred Hutchinson Cancer Research Center and School of Public Health,
University of Washington, Seattle, Washington (Scott Davis);
Department of Family Medicine and Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit,
Am J Epidemiol 2010;172:621–630
Birth Order and Risk of Non-Hodgkin Lymphoma
Michigan (Richard K. Severson); Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota (James R. Cerhan); Department of Psychiatry
and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,
California (Elizabeth C. Breen); Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and
Harvard Medical School, Boston, Massachusetts (Brenda
Birmann); and Department of Preventive Medicine, Keck
School of Medicine, University of Southern California, Los
Angeles, California (Wendy Cozen).
The current data pooling project was supported by the Leukaemia Foundation of Australia (grant 24). Individual studies
(listed in Table 1) were supported by the Italian Association
for Cancer Research and the Italian League Against Cancer
(Aviano-Napoli, Northern Italy); the Canadian Cancer Society
and the Canadian Institutes for Health Research (British
Columbia); the US National Cancer Institute (NCI) (grant
CA62006) (Connecticut); the European Commission (grant
QLK4-CT-2000-00422) (EpiLymph); the Ministry of Health
of the Czech Republic (grant MZO MOU 2005) (EpiLymphCzech Republic), the Association pour la Recherche contre le
Cancer (grant 5111) and the Fondation de France (grant 1999
008471) (EpiLymph-France); the Compagnia di San Paolo di
Torino, Programma Oncologia 2001 (EpiLymph-Italy); the
Health Research Board and Cancer Research Ireland
(EpiLymph-Ireland); the Spanish Ministry of Health, Fondo
de Investigaciones Sanitarias (grant PI 081555) and CIBERESP (grant 06/06/0073) (EpiLymph-Spain); the German Federal Office for Radiation Protection (grants StSch4261 and
StSch4420) (EpiLymph-Germany); the NCI (grant
CA51086), the European Community, and the Italian League
against Cancer (Italy); the NCI (grant CA92153) (Mayo
Clinic); the NCI (grants PC65064, PC67008, PC67009,
PC67010, and PC71105) (NCI-SEER); the American Institute
for Cancer Research (grant 99B083) (Nebraska); the National
Health and Medical Research Council of Australia (grant
990920 (New South Wales), grant 568819 to A. E. G., and
grant 510346 to C. M. V.); the NCI (grant CA69269-02) and
the Swedish Cancer Society (grant 04 0458) (SCALE); the
NCI (grants CA45614, CA89745, CA87014, and CA104682)
(University of California, San Francisco); and the Leukaemia
Research Fund of Great Britain (United Kingdom). Publication of this article was funded by the Australian Government
Department of Health and Ageing.
The funders did not participate in the design, data collection, or analyses of the individual studies, in the interpretation of the data, or in the writing of the manuscript.
The views expressed in this publication do not necessarily
represent the position of the Australian government.
The authors thank the members of the Immunology
Working Group of the InterLymph Consortium.
Conflict of interest: none declared.
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