We thank Mr. Wenbin Liang for comments on our paper.
The first part of the comments concerned criticism on our Figure 1
and handling of exposure data. Our
Figure 1 is a schematic drawing. It was aimed only to portray how the
explanatory variables precede the response variables in our two-stage
model. The purpose of our study was not to investigate does "dust exposure
increase the risk of IHD a...
We thank Mr. Wenbin Liang for comments on our paper.
The first part of the comments concerned criticism on our Figure 1
and handling of exposure data. Our
Figure 1 is a schematic drawing. It was aimed only to portray how the
explanatory variables precede the response variables in our two-stage
model. The purpose of our study was not to investigate does "dust exposure
increase the risk of IHD among patients who already had respiratory
diseases". Therefore, the figure was not intended to express that
question. The aim of our study was concentrated on the intermediate role
of the respiratory diseases in the association of dust exposure with IHD.
To predict IHD with the two-stage model we first used respiratory diseases
and dust exposure as explanatory variables. Second, we studied respiratory
diseases as response variables. The respiratory disease and exposure
variables were time dependent in the model predicting IHD just due to the
importance of the timing.
All the cohort members have been followed up until the end of the
whole follow-up period. Although the workers had moved to other jobs,
e.g., to those of lower dust exposure, they remained in our cohort.
Lifelong occupational histories (including confounding exposures) were
collected via questionnaires. In the model, cumulative exposure to dust
was considered until the diagnosis date of ischaemic heart disease (IHD)
regardless of the diagnosis date of any respiratory disease. In the model
where respiratory diseases were predicted, exposure was considered only
until the occurrence of each respiratory disease.
Changing out of dusty jobs does not remove the effect of earlier dust
exposure on IHD as well as on respiratory diseases, because both of these
diseases have developed as disease processes and are continuously
developing. The date of diagnosis is just one time point during the
development. In addition, some of the workers with a respiratory disease
had continued working in their dusty jobs.
Workers with a respiratory disease may have an increased risk to get
IHD due to an additional dust exposure after the respiratory disease
diagnosis. However, it is important to remember that those workers who
don't yet have a diagnosis but who are under the process to develop a
respiratory disease may have the same increased risk. Thus, it is more
reasonable to use the cumulative dust exposure up to the date of IHD
diagnosis. Further, if we had analyzed the exposure data only including
dust exposure after the diagnosis of a respiratory disease, the resulted
effect of dust exposure on IHD would have been small.
The most important reason for the observed small effect of dust
exposure on IHD seemed to be homogeneity in the exposure variable. This
has been thoroughly discussed in our article.
The second part of the comments concerned smoking. It is well known
that smoking is a great risk factor for both respiratory disease and IHD.
The following data on smoking were collected via questionnaires: age when
started to smoke and age when stopped, current smoking (amount of
cigarettes per day), lifelong smoking (amount of cigarettes per day,
smoking years). The comparison of the different smoking variables (tables
and models) showed that the classified variable lifelong smoking was the
most suitable for this material. Further, we have not reported any results
on the effect of interaction between smoking and respiratory diseases on
incidence of IHD. Of course we studied in the models interaction between
smoking and dust exposure as well as interaction between smoking and
different respiratory diseases but these interactions seemed to be non-
significant.
“Job strain” may be associated with unhealthy diet pattern, which
usually includes high sodium intake—a major risk factor of hypertension.
Moreover, high sodium intake is always associated with high fat and high
energy intake, and further associated with high BMI level.
Therefore, it would be interesting to see whether there is any
association between “Job constraints” and overweight among th...
“Job strain” may be associated with unhealthy diet pattern, which
usually includes high sodium intake—a major risk factor of hypertension.
Moreover, high sodium intake is always associated with high fat and high
energy intake, and further associated with high BMI level.
Therefore, it would be interesting to see whether there is any
association between “Job constraints” and overweight among the subjects in
this study.[1]
Reference
1. Radi, S., et al., Job constraints and arterial hypertension:
different effects in men and women: the IHPAF II case control study. Occup
Environ Med, 2005. 62(10): p. 711-7.
In their paper entitled “Risk of lymphatic or haematopoietic cancer
mortality with occupational exposure to animals or the public”, Svec et.
al.[1] clearly imply that they believe mortality is an acceptable
surrogate for incidence of haematological malignancy in this study group.
Although they offer certain caveats regarding this approach, they ignore
the greatest potential confounder. Patients with...
In their paper entitled “Risk of lymphatic or haematopoietic cancer
mortality with occupational exposure to animals or the public”, Svec et.
al.[1] clearly imply that they believe mortality is an acceptable
surrogate for incidence of haematological malignancy in this study group.
Although they offer certain caveats regarding this approach, they ignore
the greatest potential confounder. Patients with haematological
malignancies are, by virtue of their disease, therapy or both, very likely
to be immunocompromised, often severely. As a consequence of this
immunoparesis, these patients may be expected to be much more vulnerable
to zoonotic infections and patients with occupational contact with animals
will be much more frequently exposed to such infections than patients with
other occupations.
To validate the use of mortality as a surrogate for incidence in this
cohort, it is necessary to rule out differential mortality from infection
between cases and controls affected by haematological malignancy. There is
no evidence in this paper that Svec et. al. have considered this potential
confounding factor. Absent such consideration, it is not possible to draw
any causal inference between occupations involving animal exposure and the
risk of developing a lympho-haemopoeitic malignancy as opposed to the risk
of dying from such a condition. A further analysis would be required
considering the proximate cause of death, rather than just the underlying
cause.
Yours sincerely,
Kenneth Campbell MSc (Clinical Oncology)
Reference List
1. Svec MA, Ward MH, Dosemeci M, Checkaway H, De Roos AJ. Risk of
lymphatic or haematopoietic cancer mortality with occupational exposure to
animals or the public. Occup Environ Med 2005;62:726-35.
Exposure period is as important as the dose of exposure
Based on Figure 1 in the article[1], it could be inferred that the
timing of dust exposure would be crucial when investigating whether “Dust
exposure” would increase the risk of IHD among patients who already had
Respiratory diseases: The dust exposure of most interest would be the
exposure after the occurrence of the respiratory dise...
Exposure period is as important as the dose of exposure
Based on Figure 1 in the article[1], it could be inferred that the
timing of dust exposure would be crucial when investigating whether “Dust
exposure” would increase the risk of IHD among patients who already had
Respiratory diseases: The dust exposure of most interest would be the
exposure after the occurrence of the respiratory diseases. However people
who were already diagnosed with respiratory disease especially those in
more serious conditions were more likely to be moved away from the
cohorts, therefore a great part of dust exposure among people with chronic
respiratory diseases were likely to happen before the diagnosis of the
respiratory diseases, may appear to have minimum direct effect on IHD.
Smoking: a rope
Moreover smoking as a great risk factor for both respiratory disease
and IHD, but this study only treat smoke habits as a categorical
variable[1], it is far from enough, for example only among subjects who
are current smokers, people who have higher dose of exposure on tobacco
smoke are more likely to have both respiratory disease as well as IHD.
However an analysis only include life long non-smokers may reduce the
shadow of bias in the picture. Furthermore it is unrealistic to measure
the effect of interaction between smoking and aspiratory disease on
incidence of IHD based on the assessments of exposures in this study.
Reference
1. Koskela R-S, M.p., Sorsa J-A, Klockars M, Respiratory disease and
cardiovascular morbidity. Occup. Environ, 2005. 62.
In an interesting study published in the September 2005 issue of
Occupational and Environmental Medicine, Simoni and collegues reported the
relation between mould and/or dampness exposure and respiratory disorders
in children and adolescents in Italy [1]. The authors concluded that
wheeze and asthma can often be explained by exposure to home mould and
dampness, particularly in early life.
In an interesting study published in the September 2005 issue of
Occupational and Environmental Medicine, Simoni and collegues reported the
relation between mould and/or dampness exposure and respiratory disorders
in children and adolescents in Italy [1]. The authors concluded that
wheeze and asthma can often be explained by exposure to home mould and
dampness, particularly in early life.
Although the authors acknowledged the use of questionnaire data alone
to assess mould and dampness exposure will have limited their study, they
state that the validity of using questionnaires has been established.
In our own study, we investigated indoor exposure to dampness in 200
asthmatic and non-asthmatic children aged 4-17 [2]. We found that self-
reported dampness (by the parent/guardian) was significantly associated
with an asthmatic household, but no such association was found for
dampness observed by the field investigator or objective measures (using
an industrial dampmeter). Additionally, we have previously demonstrated
that the concordance between self-reported dampness and objective measures
is very poor [3]. In fact, there was almost complete disagreement between
self-reported dampness, visual inspection by a trained investigator and
measurement using an industrial dampmeter.
A study of the validity and determinants of reported home dampness
and moulds conducted by Dales et al reported evidence of systematic
reporting bias and recommended that objective measures rather than
questionnaires be used to clarify the health effects of indoor fungi [4].
Bearing in mind the evidence from these past studies, we feel that
the positive findings of Simoni et al should be interpreted with caution
and that all research involving home dampness should have some objective
data to back it up.
References
1) Simoni M, Lombardi E, Berti G et al. Mould/dampness exposure at
home is associated with respiratory disorders in Italian children and
adolescents: the SIDRIA-2 study. Occup Environ Med 2005; 62: 616-622.
2) Tavernier GO, Fletcher GD, Francis HC, Oldham LA, Fletcher AM,
Blacklock G, Stewart L, Gee I, Watson A, Frank TL, Frank P, Pickering CA,
Niven RM. Endotoxin exposure in asthmatic children and matched healthy
controls: results of IPEADAM study. Indoor Air. 2005;15 Suppl 10:25-32.
3) Frank TI, Pickering CAC, Fletcher G, Francis HC, Oldham LA, Kay
S, Frank P, Niven RMcL. (1999). Relationship between self reporting,
visible inspection and objective measurement of damp for determining damp
or mould contamination in houses. Proceedings of the 8th Internationional
Conference on Indoor Air Quality and Climate-Indoor Air '99, Vol. 2, pp564
-566.
4) Dales RE, Miller D and McMullen ED. Indoor air quality and
health: validity and determinants of reported home dampness and moulds.
International Journal of Epidemiology 1997; 26: 120-125.
We thank Dr. Rafnsson[1] for valuable comments on our paper.[2]
Rafnsson finds our policy implications surprising. In the light of present
evidence, we do not find further measures justified for reducing radiation
exposure among cabin crew. The justification for this view is the fact
that exposure limits common for all radiation workers, also apply for the
cabin crew. Dose monitoring indicates that the...
We thank Dr. Rafnsson[1] for valuable comments on our paper.[2]
Rafnsson finds our policy implications surprising. In the light of present
evidence, we do not find further measures justified for reducing radiation
exposure among cabin crew. The justification for this view is the fact
that exposure limits common for all radiation workers, also apply for the
cabin crew. Dose monitoring indicates that the cosmic radiation doses are
within the exposure limits. We see no reason to depart from the general
radiation protection principles.
Cohort studies have shown an excess risk of breast cancer in cabin
crew, in particular among those with long employment, with 1.5 – 3.4 fold
incidence compared with the general population.[e.g. 3] Nevertheless, the
radiation doses received are low and the expected effect based on previous
literature is very small, with RR well below 1.1.[4] Neither previous
studies nor our study have been able to identify the cause for the excess
incidence of breast cancer. Lack of association in our study does not
exclude the contribution of cosmic radiation in the development of breast
cancer, but it implies that other risk factors are likely to have a
greater role.
Rafnsson[1] finds our approach to occupational radiation dose
estimation crude. For cabin attendants, the only available source of
information on the number of flights during their career is the cabin
attendants themselves and thus, the questionnaire approach in exposure
assessment was appropriate. We used self-reported numbers of flights by
route and calendar period. We feel this is an improvement compared to
previous studies[e.g. 5-7], none of which have had any estimates of the
individual cosmic radiation dose. They have been based on surrogate
indicators such as length of employment or flight route type assignment.
Rafnsson[1] claims that we did not consider the possibility that
breast cancer can influence the subjects’ answers. Recall bias is
intrinsic in all case-control studies with subjects as source of
information and the issue was discussed in our paper.[2]
We excluded cabin attendants who worked for less than two years
because they had negligible exposure (due to very short period of cabin
work). In addition, several studies have shown that short-term employees
differ in terms of mortality and cancer risk from those with more stable
employment. Therefore, they are commonly excluded from occupational cohort
studies to avoid bias.
Our study has shortcomings inherent to retrospective case-control
study and to sparse data. Therefore, it cannot provide conclusive evidence
but does nevertheless supply new information. A large prospective follow-
up study with a large data set would be valuable. Currently, a
retrospective study, combining all the Nordic cabin crew cohorts with
comprehensive cancer incidence registration systems and improved dose
estimation algorithm is ongoing, and may be able to provide further
insight to the issue.
References
1. Rafnsson V. Retrospective assessment of exposure. Occup Environ
Med, electronic letter 25 Jul 2005.
2. Kojo K, Pukkala E, Auvinen A. Breast cancer risk among Finnish
cabin attendants: a nested case control study. Occup Environ Med
2005;62:488-493.
3. Pukkala E, Auvinen A, Wahlberg G. Incidence of cancer among
Finnish airline cabin attendants. BMJ 1995;311:649-652.
4. Boice JD Jr, Blettner M, Auvinen A. Epidemiologic studies of
pilots and aircrew. Health Phys 2000;79:576-684.
5. Haldorsen T, Reitan JB, Tveten U. Cancer incidence among Norwegian
airline cabin attendants. Int J Epidemiol 2001;30:825-830.
6. Rafnsson V, Sulem P, Tulinius H, et al. Breast cancer risk in
airline cabin attendants: a nested case-control study in Iceland. Occup
Environ Med 2003;60:807-809.
7. Reynolds P, Cone J, Layefsky M, et al. Cancer incidence in
California flight attendants (United States). Cancer Causes Control
2002;13:317-324.
We read with great interest the article by Mannes et al., which
related the adverse effects of ambient air pollution on birth weight.[1]
That article well described the effects of pollutant exposure on the risk
of low birth weight using a marker of small for gestational age (SGA).
However, that study presents some shortcomings.
First, gestational week at birth is obstetrically and sociall...
We read with great interest the article by Mannes et al., which
related the adverse effects of ambient air pollution on birth weight.[1]
That article well described the effects of pollutant exposure on the risk
of low birth weight using a marker of small for gestational age (SGA).
However, that study presents some shortcomings.
First, gestational week at birth is obstetrically and socially a more
important marker for infancy and childhood than birth weight.[2] In recent
studies such as Mannes�f, the gestational week at birth or both the
gestational week at birth and birth weight are used rather than birth
weight.[3] We are convinced that the gestational week should be
incorporated into their methods as an appropriate marker. Secondly, almost
all infants in multiple gestations are SGA even if the pregnancy course is
uneventful.[2] Accordingly, Mannes et al. were compelled to exclude
multiple gestations from the study materials. Finally, the blood-placental
barrier prevents various materials from passing through to the fetus in a
similar manner to that of the blood-brain barrier. Accordingly, it is
inferred that those materials do not easily reach the fetus even if they
can reach to the mother. Mannes et al.�fs study would have been better
researched and more useful if the above problems had been addressed in
their discussion section.
References
1) Mannes T, Jalaludin B, Morgan G et al. Impact of ambient air
pollution on birth weight in Sydney, Australia. Occup Environ Med
2005;62:524-30.
2) Cunningham FG, Leveno KJ, Bloom SL et al. Williams Obstetrics (22nd
edn) TX, McGraw-Hill 2005
3) Wiles NJ, Peters TJ, Leon DA et al. Birth weight and psychological
distress at age 45-51 years: results from the Aberdeen Children of the
1950s cohort study. Br J Psychiatry 2005;187:21-8.
The report on the occupational health risks of ethylene glycol
ethers is convincing while using the time-honoured indicators of female
reproductive health.[1]
The effects often coincide with or depend on nervous system toxicity of
e.g. solvents. The toxic effect of the ethylene glycol ethers seems to
stem from their end metabolites, the corresponding alkoxyacetic acids.
They seem to be inhibit...
The report on the occupational health risks of ethylene glycol
ethers is convincing while using the time-honoured indicators of female
reproductive health.[1]
The effects often coincide with or depend on nervous system toxicity of
e.g. solvents. The toxic effect of the ethylene glycol ethers seems to
stem from their end metabolites, the corresponding alkoxyacetic acids.
They seem to be inhibitors of succinate dehydrogenase [2,3] which is
critically associated with mitochondrial respiratory chain and links it
with the tricarboxylic acid circle. Thus, one would expect negative
consequences of exposure in highly oxygen-dependent organs, like brain and
kidneys. Chronic energy failure would also be harmful to the developing
foetus.
Kojo et al. [1] report their results on breast cancer risk among
airline cabin attendants in a nested case-control study. Increased
incidence of breast cancer has been repeatedly found among Finnish and
other airline cabin attendants and that is the motive of the study. The
results do not support the hypothesis that cosmic radiation exposure as
measured in the study is strongly linked to the inductio...
Kojo et al. [1] report their results on breast cancer risk among
airline cabin attendants in a nested case-control study. Increased
incidence of breast cancer has been repeatedly found among Finnish and
other airline cabin attendants and that is the motive of the study. The
results do not support the hypothesis that cosmic radiation exposure as
measured in the study is strongly linked to the induction of breast cancer
among the cabin attendants. I would like to comment on some aspects of
this study as well as how the results are interpreted. A cohort of Finnish
cabin attendants serves as a study base, cases of breast cancer were found
in the Finnish Cancer Registry and controls were chosen with matching on
year of birth from non-cases of the cohort. This setting seems to be ideal
and it should be possible to identify all cases of breast cancer that
occur in the study base. However, exposure information in the study was
collected after the cases of breast cancer have been diagnosed. This
procedure will influence validity because it opens up the possibility that
breast cancer may influence the access and the quality of the information
on exposure. This is exactly what happens in the study and I will come to
it later.
In the paper Kojo et al. state the following policy implications:
“There is no need to take occupational factors into account in breast
cancer prevention among cabin attendants.”[1] This is a surprisingly
determined generalisation in the light of the small material of the study.
The study has not convincingly demonstrated the absence of effect of an
occupational exposure on breast cancer risk among cabin attendants. There
is a well known definition of a negative study but the study of Kojo et al.
[1] does not fit into that definition. A true negative study must be large
and sensitive, and it must have accurate exposure data.[2] It seems to me
that the study of Kojo et al. is lacking in all three aspects.[1] I would
like to point out the following: The authors themselves discuss the
smallness of the material consisting of 27 cases. It also seems a rather
crude method to estimate cumulated radiation dose on basis of, among other
parameters, five questions on number of round-trip flights per month
divided in two or three decades. Further there was only 52% participation
rate in the questionnaire survey on exposure. The limitation of collecting
exposure information retrospectively has been addressed in a recent study
on airline cabin attendants.[3] That study did not suffer from low
participation.
Kojo et al. [1] have a problem with possible selection bias because of
poor participation when estimating the exposure to cosmic radiation. In an
attempt to evaluate this problem they calculate the odds ratio for breast
cancer for all subjects in the cohort (44 cases and 921 non-cases, all
subjects with known start and end of cabin work according to information
obtained from Finnair and the Finnish Cabin Crew Union). Based on this
information they calculate active work year and combine this with the
estimated mean annual cosmic radiation dose by calendar period [4] to
obtain a crude estimate of cosmic radiation dose for every person
explained in the Methods.[1] Not surprisingly they get a similar odds
ratio in the matched case-control study as in the analysis when they
calculate the odds ratio using the crude cosmic radiation exposure data in
the Results.[1] Both exposure estimates involve information from the
questionnaire survey with the poor participation rate. However, this fact
does not keep the authors from concluding on the similarity of the two
odds ratio in the Discussion.[1] Here we seem to be facing a phenomenon
called arguing in a circle. Arguing in a circle occurs when two or more
unproved propositions are used to establish each other. In this aspect I
would like to point out the interesting difference in the mode of
expression concerning these odds ratio in the Result section.[1] In the
main study with the smaller number of subjects in the univariate analysis
it is: “..cumulative radiation dose …..showed no effect on breast cancer”,
and in the multivariate analysis it is: "..cumulative radiation
dose...showed negligible effects on breast cancer", whereas the odds ratio
based on calculation on crude work years gets: “..using crude cosmic
radiation exposure data, the occupational radiation dose was not
associated with breast cancer…”.
The authors mention the limitation of their study due to
retrospective collection of the information on exposure. The case
ascertainment was retrospective and therefore eight cases (18%) were
deceased and lost from the study and authors assume the loss to be greater
among breast cancer cases due to excess mortality from breast cancer
compared to non-cases. This can indeed be calculated from the data given
in the paper to be 24 deaths or 2% of the non-cases. Thus proportionally
more cases than non-cases were lost because they were deceased and thus it
was not possible to collect information from them in regard to exposure.
In the study of Kojo et al. [1], breast cancer influences the access to
information on exposure and this has possibly introduced bias.
Kojo et al. [1] do not mention the possibility that the disease,
breast cancer, can influence the subjects answers, which may introduce
errors.[2, 5] This may particularly occur if cabin attendants who get
breast cancer or the non-cases suspect that there is an association
between occupation and cancer and such suspicion can easily arise based on
information via mass media if not from other sources. No information is
given in the paper on measures taken to avoid possible influence by the
authors knowledge of the aim of the study. There is no comment on whether
investigation on the exposure conditions were conducted blind as to the
case-control status of the cabin attendants. For example it is not very
clear who selected and how the representative routes were chosen. These
routes were later used to calculate radiation dose.[4] One can only
speculate whether these have introduced bias. However, the authors
excluded from the study those who had worked for less than two years as
airline cabin attendants and it appears not to have involved cases. This
exclusion based on exposure variable introduces bias towards the null
hypothesis.
In the conclusion of the Abstract and in the last sentence of the
Main messages it says that three occupational factors are studied and it
is stated that there is no clear evidence that they affected the breast
cancer risk. It is not a simple task to find where in the paper the
authors give a clear account of all three occupational factors, however,
it is easy to identify the cumulative radiation dose in mSv as an
occupational factor. The other two occupational factors are the disruption
of the sleep rhythm and the disruption of the menstrual cycle.
Inexperienced reader may be confused whether these factors belong to
outcome or exposure and the same may be valid for cases and non-cases. Are
these disturbances, identified with the other exposure data
retrospectively, so clearly related to the occupation as to serve as a
surrogate of exposures? Is it possible to escape sleep disturbances as a
consequence of long haul flight? Is it possible that breast cancer cases
get sleep disturbances of causes other than occupational? Is it possible
that these disturbances are not suitable exposure indicators?
Kojo et al. [1] divided the material into two parts in an attempt to
evaluate the possible longer recall period concerning flight activity,
disturbances of menstrual cycle and sleeping among those over 50 years of
age as compared to younger women. The next step was to calculate odds
ratio for breast cancer in the two groups (50 years of age and younger,
and over 50 years of age) associated with each of the three factors
separately i.e. cumulative radiation dose, sleep disturbances, and
menstrual disturbances. These calculations, with less than 27 cases in
each group, (the number in each group is not available in the paper) yield
six different odds ratio and wide 95% confidence intervals, which all
include unity. However, the authors conclude that the estimates were
comparable suggesting that the lower participation among those older than
50 years did not bias the results. The longer recall for the older women
is not mentioned in this respect, which was the goal in the outset. Here
the authors conclude firmly based on small material arguing for the
validity of their study. In this discussion, on what Kojo et al. [1] call
modifying effect of age, we are shown the range of exposure in the groups,
0-103.5 mSv for women 50 years of age and younger, and 0-136.8 mSv for
women over 50 years of age. It is rather confusing to see the range go
down to zero, given that airline cabin attendants with less than two years
career were excluded from the study.
In the Discussion Kojo et al. [1] inform us on the fact that the
excess risk in the incidence of breast cancer among Finnish cabin
attendants has persisted based on updated follow up. They suggest that
this risk is related to well known risk factors of breast cancer such as
family history of breast cancer and possibly to moderate or heavy alcohol
consumption. And the authors do not compare their findings with
information from other studies on breast cancer among cabin attendants
ignoring the benevolent recommendation given long ago.[5]
References
1. Kojo K, Pukkala E, Auvinen A. Breast cancer risk among Finnish
cabin attendants: a nested case-control study. Occup Environ Med,
2005:62;488-493.
2. Hernberg S. Introduction to Occupational Epidemiology. Chelesea:
Lewis Publisher, 1992.
3. Grajewski B, Atkins DJ, Whelan EA. Self-reported flight hours vs.
company records for epidemiologic studies of flight attendants. Aviat
Space Environ Med, 2004;75:806-810.
4. Kojo K, Aspholm R, Auvinen A. Occupational radiation dose
estimation for Finnish aircraft cabin attendants. Scand J Work Environ
Health, 2004;30:157-163.
5. Breslow NE, Day NE. Statistical methods in cancer research, Vol. I.
The analysis of case-control studies. Lyon: International Agency for
Research on Cancer, 1980.
Kyle Steenland raises some interesting points in his commentary on silica [1] both on our papers reporting exposure assessment and mortality in the UK silica sand industry [2,3] and on the adverse effects of silica in general.
With the exception of one quarry, where other exposures such as polycyclic aromatic hydrocarbons could have occurred, no relationship was found with cumulative silica ex...
Kyle Steenland raises some interesting points in his commentary on silica [1] both on our papers reporting exposure assessment and mortality in the UK silica sand industry [2,3] and on the adverse effects of silica in general.
With the exception of one quarry, where other exposures such as polycyclic aromatic hydrocarbons could have occurred, no relationship was found with cumulative silica exposure in the UK silica sand study. Steenland points out that the estimated exposure levels were relatively low with only a few workers having a cumulative exposure over 1
mg/m3.years (in fact only 8% of the study population overall and 5 lung cancer cases had a cumulative exposure over 2 mg/m3.years). We agree that this may be why only 2 silicosis deaths were observed, although, as we point out in the paper, fibroses, including silicosis, are poorly recorded
on death certificates and therefore could not be accurately assessed.
Steenland draws attention to the current controversy over the relationships between silica, silicosis and lung cancer. The epidemiological literature is indeed inconsistent. OEM readers might be interested to know about a two day workshop organised by the European Association of Industrial Silica Producers (EUROSIL) that was held in New
York in August 2004, which brought together leading scientists from Europe, the USA, Canada, China, South Africa and Australia involved with the major studies in the industrial sand, diatomaceous earth, mining, heavy clay, granite, stone, pottery and brick industries [4] (Kyle
Steenland was invited but was unfortunately unable to attend). The aim of the workshop was to gain a clear understanding of the epidemiological work to date, and to prioritise future research needs. Following brief presentations summarising the results from these studies, break out groups evaluated the variations between studies in the design, definition and derivation of health outcomes, assessment of exposure, collection of confounding data and statistical methodology. The groups identified the knowledge gaps and discussed the feasibility and desirability of filling these.
The overwhelming conclusion from the workshop was that heterogeneity occurs across all aspects of both the actual nature of the industries in which silica exposure occurs and the design, conduct, analysis and interpretation of the studies that have been carried out.
Heterogeneity in one or more of the following might contribute towards the differing results:
The physico-chemical features of the silica, including geological species, chemical composition, percentage crystalline silica, particle size, freshness of the fracture.
Methods of measuring silica exposure, including sampler design, analytical technique, sampling strategy.
Exposure assessment methodology, including taking account of changes in technology, use of protective equipment such as respirators, retrospective extrapolation.
Derivation and definition of health outcomes, including accuracy and completeness of death, cancer and other registers, diagnostic procedures such as X rays, CT scans, use of pathological samples.
Methodology for collection of data on confounding variables such as smoking, which, in many studies, is limited.
Statistical methodology, including study size, follow-up time, adjustment for confounders and other relevant exposures, appropriate models.
Many of the above are, of course, inherent difficulties in occupational epidemiology in general. However, in the area of silica some of these differences may be so fundamental that it may be necessary to rethink the concept of the silica industry being a uniform entity. As the
IARC Working Group pointed out, given the wide range of populations and exposure circumstances some non-uniformity of results would be expected [5] and even within the same industry the results from different studies may vary. Some of these are highlighted below.
At the workshop the group discussing studies in the diatomaceous earth industry highlighted uncertainties in exposure assessment methodologies including the conversion factors used to convert total dust counts to respirable silica, extrapolation methods used for exposures prior to 1950 and lack of adjustment for calcining for exposure before 1930. They were also concerned about co-exposures to asbestos, lack of smoking data, and the difficulty of separating small round opacities from small irregular opacities when reading chest x-rays. Conversion factors
and extrapolation in exposure assessment were also of concern regarding the studies in the sand industry as was the origin and composition of the sand. For example, in the North American studies some sands were almost pure quartz, whilst others elsewhere were dune sand or feldspar sands.
There were also differences in the aluminium content. Workforces in mining were considered to have the advantages of large and stable workforces, good exposure measurement data and special health programmes and surveillance. Co-exposures, for example, to radon, asbestos and PAHs were
highlighted as particular problems, however, as was the variation in the quartz content of dusts from mines extracting tin, gold, or coal.
The group discussing the mining studies also drew attention to the need to investigate exposure metrics such as intensity and peaks of exposure as an alternative to cumulative exposure. Many of the above considerations were also identified as potential problems in the pottery, brick and granite industries. The researchers involved in the Vermont granite studies expressed interest in collaborating to explore the reasons why their studies had produced different results. This group drew attention to the issues of survivor populations and susceptibility. For example, in the studies in China, there is a high incidence of non-malignant respiratory disease at an early age that could impact on the results for lung cancer occurring at older ages.
One of the aims of the workshop was to try and target any future research so that key issues concerning adverse health effects of silica and uncertainties around current knowledge can be addressed. These include:
In what industrial settings, if any, does silica exposure at
current compliance levels, essentially at established ‘Western (US, EU)’ limits, cause cancer?
What role does silicosis (fibrosis) play?
Does the lung cancer risk increase for radiographic severity of silicosis?
Are there pathological differences between fibrosis from silica and asbestos that influence lung cancer?
How does non-malignant respiratory disease affect lung cancer risks?
From the various presentations and discussions, eight priorities were
identified:
1. Effectively control workers’ dust exposure and implement proper
evaluation and prevention measures.
2. Harmonise sampling and analytical methods for future collection of
dust measurements and develop a standardised job/task industry wide Job
Exposure Matrix (JEM).
3. In parallel, collect information on the type and use of personal
protective equipment and develop the methodology for incorporating this
into the JEM for future exposure assessment.
4. Investigate the toxicological potency of different types of silica
using industry samples
5. Focus on industries with similar exposures and review the
differences that may have given rise to different estimates of risk.
6. Consider whether pooling of the data might be useful and
investigate what this might entail, e.g. development of a harmonised JEM
and exposure assessment methodology, bearing in mind that indiscriminate
pooling might give misleading and imprecise results.
7. Consider whether current cohorts might be able to re-analyse their
data to address the priority areas of concern and/or whether they can
collect supplementary data to assist with this.
8. Alternatively carry out a new study(ies) but ensure that there is
an agreed protocol and a design that ensures knowledge gaps will be
filled.
Some of the above are already being developed. For example, the
Industrial Minerals Association in Europe is developing a harmonised dust
monitoring strategy and encouraging all its members to implement this.
IARC have classified silica as a Group 1 carcinogen, but rather than
focusing on hazard identification, workshop participants felt that the
focus should be on reducing exposure to a level that would protect against
silicosis, and thus probably lung cancer, and be technically achievable
across the whole industry. The need for enforcement of current national
standards is illustrated by findings of high percentages of samples taken
by the US Occupational Safety and Health Administration (OSHA) still
exceeding the OSHA Permissible Exposure Limit (PEL) of 0.1 mg/m3 [6]. For
example in 1999, 47% and 38% of samples in construction and manufacturing
groups, respectively, exceeded the OSHA PEL. The fact that silicosis (and
other potentially related diseases) still occurs is likely to be due to
the continuation of overexposure that is perhaps not reflected in some of
the industries investigated in much of the current epidemiological
literature, which tends to focus on producers rather than manufacturers or
users.
References
1. Steenland K Silica: déjà vu all over again? Occup Env Med 2005; 62: 430-432
2. Brown TP, Rushton L Mortality in the UK Industrial Silica Sand
Industry: 1. Assessment of exposure to respirable crystalline silica.
Occup Env Med 2005; 62: 442-445
3. Brown TP, Rushton L Mortality in the UK Industrial Silica Sand
Industry: 2 a retrospective cohort study. Occup Env Med 2005; 62: 446-452
4. Eurosil Proceedings of expert workshop: Epidemiological
perspectives on silica and health. Brussels, Belgium, European Association
of Industrial Silica Producers, 2005
5. IARC Monographs on the evaluation of carcinogenic risk to human,
vol. 68: silica, some silicates, coal dust and para-aramid fibrils. Lyon,
International Agency for Research on Cancer, 1997
6. NIOSH Work-related lung disease surveillance report 2002 (DHHS
(NIOSH) Number 2003-111) Cincinnati, Ohio, National Institute for
Occupational Safety and Health, 2003
Dear Editor,
We thank Mr. Wenbin Liang for comments on our paper.
The first part of the comments concerned criticism on our Figure 1 and handling of exposure data. Our Figure 1 is a schematic drawing. It was aimed only to portray how the explanatory variables precede the response variables in our two-stage model. The purpose of our study was not to investigate does "dust exposure increase the risk of IHD a...
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“Job strain” may be associated with unhealthy diet pattern, which usually includes high sodium intake—a major risk factor of hypertension. Moreover, high sodium intake is always associated with high fat and high energy intake, and further associated with high BMI level.
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Exposure period is as important as the dose of exposure
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Kyle Steenland raises some interesting points in his commentary on silica [1] both on our papers reporting exposure assessment and mortality in the UK silica sand industry [2,3] and on the adverse effects of silica in general.
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