This article is presenting both the applied question and the
statistical methods used in a very well organised and excellent way.
Though, I have some comments on the use of the words risk, effect and
predictor as these imply a casual relationship and make us believe that we
model the development of neck pain.
The data in the article is longitudinal and in the analysis the
authors are using this...
This article is presenting both the applied question and the
statistical methods used in a very well organised and excellent way.
Though, I have some comments on the use of the words risk, effect and
predictor as these imply a casual relationship and make us believe that we
model the development of neck pain.
The data in the article is longitudinal and in the analysis the
authors are using this. In the marginal model all repeated measures of
“current neck pain” are included and time is in the model. In the
transition model “current neck pain” is modelled and “neck pain previous
year” is included in the model. These are interesting and valuable models
that gives more information on the association of interest than a cross-
sectional study would, but it is not the same as modelling risk or
incidence.
The two models used and the analysis of them is correct as long as
one sees that they are not models of the risk of developing neck pain.
This fact is not clearly put forward either in this article or in texts
about analysis of longitudinal ordinal data. The fact that the data is
longitudinal ensures us that there is more information than data from a
cross-sectional study can give. Note though that the fact that the data is
longitudinal does not automatically imply that it is possible to analyse
cause and effect or to analyse the risk of developing neck pain.
The marginal model in the article models the prevalence of neck pain
using all the information in the repeated measures, time trend etc. The
transition model is modelling the prevalence of neck pain and all the
explanatory variables are adjusted for “previous neck pain”. That is the
OR, for example of “mental stress”, is the OR for having neck pain now,
averaged over all possible values of “previous neck pain”. Note though
that this is not the same as modelling the development of neck pain, in
other words the risk of getting neck pain. In the transition model one
interaction is included, “previous neck pain” and “hand above shoulder”.
This means that for the specific explanatory variable “hands above
shoulder” one can talk about the effect on moving from severe to mild neck
pain (developing neck pain) or the other way around due to “hands above
shoulder”.
The topic of analysing longitudinal ordinal data as an alternative to
dichotomising variables and analysing them traditionally is very
interesting and needs further discussions. There are statistical models
and methods available, but to realise what we model is of great
importance.
I want to thank the authors for a nice article and for introducing
these models in the area of occupational medicine. I look forward to
further discussions and developments in the methodological area of
longitudinal ordinal data.
We appreciate Dr. Kromhout’s comments regarding our article “Air
samples versus biomarkers for epidemiology”[1] and are pleased that he
supports our recommendation that both air samples and biomarkers be
collected whenever possible. Kromhout raises three points in his letter.
First, he suggests that our conclusion that biomarkers tend to be better
surrogates for exposures than air samples might have b...
We appreciate Dr. Kromhout’s comments regarding our article “Air
samples versus biomarkers for epidemiology”[1] and are pleased that he
supports our recommendation that both air samples and biomarkers be
collected whenever possible. Kromhout raises three points in his letter.
First, he suggests that our conclusion that biomarkers tend to be better
surrogates for exposures than air samples might have been biased by our
inclusion of some data from studies that contained only biomarker
measurements. Second, he questions our comparisons of air measurements
and biomarkers in situations where sample sizes differ between the two
types of data. And finally, Kromhout argues that because variance ratios
(values of lambda) are relative measures, they should not be the sole
criteria for choosing between air and biological measurements in an
epidemiology study. We will respond to each of these points in turn.
We agree that it is best to compare environmental and biomarker data
that were collected concurrently from the same subjects. Indeed, as we
indicated, we made every effort to do so and were successful in 36 of the
47 biomarker datasets (77%) listed in table 1. We have no reason to
believe, as Kromhout suggests, that the remaining 11 biomarker datasets
were less representative of biomarker measurements than those with
concurrent air measurements. But, even if we restrict our analyses to
datasets with concurrent air and biological measurements, 62% of these
datasets had lambda ratios less than one (median=0.46, p.755, par. 2),
indicating that a typical biomarker should be a less biasing surrogate for
exposure in an epidemiology study than a typical air measurement. This
was the main conclusion of our analyses. Although sample sizes were small
for stratified comparisons, we also observed that lambda ratios for metals
were significantly smaller than one (Figure 5B), while those for pesticide
exposures were significantly greater than one. Although Kromhout
concluded from the latter result that air measurements would be favoured
for pesticides, we cautioned that a lambda ratio greater than one could
also result from multiple routes of pesticide exposure including
inhalation, dermal contact, and ingestion; if this were the case, then
biomarkers would be preferred for pesticide exposures as well.
Regarding the issue of sample sizes, Kromhout suggests that the variance
components estimated in our study might have been influenced by the
relative numbers of air and biomarker measurements in our database. His
argument is based upon previous work, which indicated that the within-
subject variance components, estimated from air measurements of workplace
exposures, tended to be greater when sample sizes were greater than 10
measurements than when they were less than 10 measurements’.[2] In fact,
the median number of repeated observations from a given subject in our
database was three for both biomarkers (range: 2-30) and air measurements
(range: 2-37). So, different sample sizes were unlikely to have
influenced our results. We also note that the variance components
estimated in our study were obtained after adjustment for fixed time
effects, whereas those from Kromhout’s study were not.[2] Because sample
sizes tend to be positively associated with the duration of a study,
larger numbers of measurements tend to suggest longer periods of
observation which can introduce trends and seasonal effects into the data.
Indeed, in our study we identified significant fixed effects of time in
about 1⁄2 of the sets of biomarker measurements and about 1/3 of the sets of
air measurements. We reported in our paper that ignoring such fixed time
effects tended to increase the estimated within-person variance component
and to decrease the estimated between-person variance component,
consistent with the earlier work of Symanski et al.[3]
Concerning the relative nature of the variance ratio (lambda), we agree
that it should not be the sole criterion used to choose between air and
biological measurements. As we indicated on p.758 of our paper, “…the
optimal measure of exposure for an epidemiology study depends not only on
variance ratios of the air and biomarker measurements (smaller is better),
but also on projected sample sizes (larger is better), based on practical
considerations and costs, and knowledge of the dominant route of exposure
(if multiple routes, biomarkers are preferred).”
Finally, Kromhout questioned the numbers of datasets we used for
various analyses and noted a discrepancy regarding the numbers of datasets
referred to in Figures 4C and 5C. His confusion may have resulted from
the fact that some studies reported multiple biomarkers for a given air
exposure (e.g., reference 24, appendices B and D) and, occasionally, two
types of air exposure for a given biomarker (e.g., ref 26, appendices B
and D). Thus, 43 datasets generated 64 estimates of lambda for biomarkers
(Figure 4) and generated 54 lambda ratios (Figure 5). While rechecking
our files, we found that one set of blood lead measurements had been coded
as an intermediate-term biomarker for the estimate of lambda (Figure 4C)
and as a long-term biomarker for the estimate of the lambda ratio (Figure
5C). We also discovered that an asterisk (indicating statistical
significance) had been inadvertently deleted from Figure 5B under the bar
‘Metal’. We apologize for any confusion this may have caused.
Sincerely,
S.M. Rappaport, Ph.D.
Yu-Sheng Lin, Ph.D.
Lawrence L. Kupper, Ph.D.
School of Public Health
University of North Carolina
Chapel Hill, NC 27599-7431
U.S.A.
References
1. Lin YS, Kupper LL, Rappaport SM. Air samples versus biomarkers for
epidemiology. Occup Environ Med. 2005 Nov;62(11):750-60.
2. Kromhout H, Symanski E, Rappaport SM. A comprehensive evaluation of
within- and between-worker components of occupational exposure to chemical
agents. Ann Occup Hyg. 1993;37(3):253-70.
3. Symanski E, Kupper LL, Kromhout H, Rappaport SM. An investigation of
systematic changes in occupational exposure. Am Ind Hyg Assoc J.
1996;57(8):724-35.
Sir, the recent paper by Lin et al.[1] in the November issue of the
journal was a thought provoking piece of work. In their paper the authors
try to prove the theoretically derived hypothesis that biomarkers of
exposure have smaller variance ratios and would typically provide less
biased surrogates of exposure compared to air measurements. Although I
entirely agree with the theoretical part of this s...
Sir, the recent paper by Lin et al.[1] in the November issue of the
journal was a thought provoking piece of work. In their paper the authors
try to prove the theoretically derived hypothesis that biomarkers of
exposure have smaller variance ratios and would typically provide less
biased surrogates of exposure compared to air measurements. Although I
entirely agree with the theoretical part of this scientific discussion, I
do not think the data presented in their paper actually support the
notion.
I would like to raise three issues to support my case. First, in
order to proof the hypothesis one would have to compare concurrently
collected environmental and biological data gathered from the same
individuals during the same time period. The main analysis presented in
the paper by Lin et al.1 does not fulfil this criterion, because as the
authors pointed out datasets were added with biomarker data only. These
added datasets might not have been representative for the other situations
considered and as the authors point out might be from exposure situations,
which favours biomonitoring a priori (exposure with a long residential
half-life). When we restrict ourselves to the data presented in Figure 5
we see no statistically significant difference in variance ratios between
the two measures of exposure except for pesticides, but these exposure
situations remarkably show variance ratios that favour air measurements.
There is also a puzzling issue when one compares table 3, figure 4 and
figure 5. From figure 4 one learns that 64 lambda values could be
estimated for biomarker measurement datasets and 43 for air measurement
datasets. One would expect that when the authors fall back to exposure
settings where both measurements were performed, they would present
results for 43 datasets. However, in figure 5 results are presented for 54
datasets. It becomes even more puzzling when one considers table 3 where
33 air measurement datasets and 46 biomarker measurement datasets are
mentioned. In addition, comparing figure 4c with 5c it is remarkable that
the number of datasets with biomarkers with intermediate residence time
increases from 17 to 18. Unless the residence time of the biomarkers was
recoded this would have been impossible.
The second issue that might have biased the presented results is the
unequal number of both monitored workers and occasions. From table 1 and
Appendices A and B it is clear that in most situations more air samples
were collected than biomarker samples. As was shown earlier[2], when the
number of observations increases (more individuals measured on more
occasions covering a larger area of possible conditions) the estimated
between- and within-worker variance components have a tendency to increase
as well. So if one does not restrict the datasets to the same individuals
measured over the same period, comparing variance ratios of air and
biomarker data becomes problematic. From the Appendices it looks like that
in most cases the data collection covered the same period, but not
necessarily the same individuals (see for instance reference 24 in
Appendix B, where 249 airborne styrene measurements were performed on 48
individuals during a period of 2-3 months, while 146 blood styrene
measurements were taken from 29 individuals in a period of 3-4 months).
The third issue I would like to raise is the comparison of
environmental versus occupational exposures. Based on the presented fold
ranges and lambda values the authors claim “biomonitoring may be more
advantageous in environmental settings than in occupational settings”.
Here the authors appear to forget that fold ranges and lambda’s are by
definition of a relative nature. A fold range of 100 at nanogram level
might be biologically totally irrelevant, while a fold range of 2 around a
level where biological effects can be expected, might be relevant to study
in an epidemiological setting. When we studied a group of pig farmers in
The Netherlands3 their between-farmer fold range in average air exposure
to endotoxins was very low (bwR95=4.1) and the variance ratio such that
we could expect heavily biased exposure response relations (λ= 4.7).
Nevertheless even within a group so uniformly exposed meaningful exposure-
response relations could be discerned by applying an innovative exposure
assessment method where measurement results were modelled and exposure
predicted based on diary information.
Finally, I would like to comment and I am convinced that the authors
will fully agree, that one should not choose between air sampling and
biomonitoring, but one should perform both whenever possible. Not only
because more data is needed to prove the case for biomonitoring (for which
currently data is insufficient), but also air and dermal measurements are
a necessity in order to link (internal) exposure to possibilities for
hazard control. Prevention of occupational and environmental exposures
before they reach harmful concentrations will continue to be our first
priority in the field of occupational and environmental health.
References:
1. Lin YS, Kupper LL, Rappaport SM. Air samples versus biomarkers for
epidemiology. Occupational and Environmental Medicine 2005; 62:750-60.
2. Kromhout H, Symanski E, Rappaport SM. A comprehensive evaluation of
within- and between-worker components of occupational exposure to chemical
agents. the Annals of Occupational Hygiene 1993; 37:253-270.
3. Preller L, Kromhout H, Heederik D, Tielen MJM. Modeling long-term
average exposure in occupational exposure-response analysis. Scandinavian
Journal of Work Environment and Health 1995; 21:504-12.
We have read the study on respiratory disease and cardiovascular
morbidity by Koskela and coworkers with great interest.[1] They found no
obvious effect of direct dust exposure on ischaemic heart disease (IHD)
among granite workers and workers in metal industry such as foundry
workers and iron foundry workers in Finland. Furthermore, there was a weak
association between dust exposure and chronic bronc...
We have read the study on respiratory disease and cardiovascular
morbidity by Koskela and coworkers with great interest.[1] They found no
obvious effect of direct dust exposure on ischaemic heart disease (IHD)
among granite workers and workers in metal industry such as foundry
workers and iron foundry workers in Finland. Furthermore, there was a weak
association between dust exposure and chronic bronchitis and
pneumoconiosis, respectively, among granite workers. However, there was an
association between chronic bronchitis and IHD among granite workers and
iron foundry workers.
We want to make one comment regarding a possible bias towards unity-
effect by the inclusion of non-exposed workers.
The study by Koskela et al. comprises six cohorts. Three of these
cohorts consist of 1000 iron foundry workers, 1000 metal product workers
and 1000 electrical workers, respectively. The subjects selected into the
cohorts were 400 current and 400 former male workers with the longest
duration of employment and a further 200 with the shortest duration of
employment.
A possible association between air pollutants and IHD may have a
similar mechanism as the association between smoking and IHD. Smoking more
than 25 cigarettes per day is estimated to double the risk of IHD compared
with non-smokers. This risk decreases after quitting smoking and becomes
closer to the risk of non-smokers.[2] Thus, former dust exposed workers
followed for 16 years may ultimately have a risk similar to non-exposed
workers.
Consequently, the findings of the study by Koskela and coworkers may
underestimate the relation between dust exposure and the occurrence of
IHD. We certainly agree with our Finnish colleagues that efforts to
prevent IHD should both include the prevention of respiratory diseases and
the control of dust exposure.
We do not have any competing interest regarding this letter.
References:
1. Koskela R-S, Mutanen P, Sorsa J-A, Klockars M. Respiratory disease
and cardiovascular morbidity. Occup Environ Med 2005; 62: 650-655.
2. Doll R, Peto R, Wheatley K, Gray R, Sutherland I. Mortality in
relation to smoking: 40 years’ observations on male British doctors. BMJ
1994; 309: 901-911.
Bengt Sjögren, MD, PhD
Work Environment Toxicology
Institute of Environmental Medicine
Karolinska Institutet
P.O. Box 210
SE-171 77 Stockholm
Sweden
Tel: 46 8 524 822 29
Fax: 46 8 31 41 24
E-mail: Bengt.Sjogren@ki.se
Carl-Göran Ohlson, MD, PhD
Department of Occupational and Environmental Medicine
Örebro University Hospital
SE-701 85 Örebro
Sweden
Tel.: +46 19 6022468
Fax.: +46 19 120404
E-mail: carl-goran.ohlson@orebroll.se
We read with interest the article by Sorahan et al., “Cancer risks in a historical UK cohort of
benzene exposed workers” [1]. We note that the authors showed an increased SMR and SRR for
lung cancer among this group. They comment that “there was evidence of increased mortality
for lung and lip cancers and for ANLL, and increased morbidity for lung and pleural cancers.
There is no reason to suspect that benze...
We read with interest the article by Sorahan et al., “Cancer risks in a historical UK cohort of
benzene exposed workers” [1]. We note that the authors showed an increased SMR and SRR for
lung cancer among this group. They comment that “there was evidence of increased mortality
for lung and lip cancers and for ANLL, and increased morbidity for lung and pleural cancers.
There is no reason to suspect that benzene is responsible for the increased lung and pleural
cancer risks in this study.” The authors then go on to point out that it is likely that some
members of the cohort were exposed to other lung carcinogens. However, we feel that if this
was a likely explanation for the observed excess, then the SMR for lung cancer would probably
have been heterogeneously distributed among the industries studied, the risk being elevated only
in those industries with known exposures to lung carcinogens. This does not seem to have been
the case. Further , confounding by cigarette smoking seems unlikely because the SMR for some
other tobacco-related causes are not significantly elevated (e.g. non-maliganant diseases of the
respiratory system).
There have been two papers based on a Chinese cohort of benzene exposed workers [2,3], which
are relevant to this issue. The first of these, showed an RR for lung cancer of 2.31 among
exposed non-smokers, and while 95% confidence intervals were not reported the authors did
state this was statistically significant [2]. The second reported a relative risk for lung cancer of
1.4 (95% CI 1.0-2.0) among benzene exposed males [3]. Therefore, we think it is important to
recognize that while the excess described in this paper may be due to exposure to other
carcinogens, it is also possible that we are seeing accumulating evidence of an association
between benzene exposure and lung cancer.
References
1 Sorahan T, Kinlen LJ, Doll R. Cancer risks in a historical UK cohort of benzene exposed
workers. Occupational and Environmental Medicine 2005; 62:231-236.
2 Yin SN, Li GL, Tain FD, et al. A retrospective cohort study of leukemia and other
cancers in benzene workers. Environmental Health Perspectives 1989; 82:207-13.
3 Yin SN, Haze RB, Linet MS, et al. A cohort study of cancer among benzene exposed
workers in China: Overall results. American Journal of Industrial Medicine 1996;
29:227-35.
We thank Helen C Francis for the interest in our article
“Mould/dampness exposure at home is associated with respiratory disorders
in Italian children and adolescents: the SIDRIA-2 Study” [1] and we
appreciate her comments reported in the letter “The validity of self-
reported measures of mould/dampness”, 21 September, 2005.
We think it is difficult to compare our findings with those of Tavernier
and co...
We thank Helen C Francis for the interest in our article
“Mould/dampness exposure at home is associated with respiratory disorders
in Italian children and adolescents: the SIDRIA-2 Study” [1] and we
appreciate her comments reported in the letter “The validity of self-
reported measures of mould/dampness”, 21 September, 2005.
We think it is difficult to compare our findings with those of Tavernier
and colleagues [2] for the following reasons:
1. that study regards a relatively little (n=200) sample of subjects,
aged 4 to 17 years, whereas we studied thousands of children and
adolescents, separately;
2. that study regards current exposure, whereas we compared the
effects of current and early exposure.
In addition, as regard the current exposure, the findings by
Tavernier and colleagues do not seem to disagree with our results. We also
did not find a significant association between asthma and current
exposure, among the adolescents, and the association was not so strong, as
indicated by 95%CI (1.00-1.93), among the children.
It is not surprising to find controversial results in the literature.
Although some studies showed a poor concordance between self-reported
dampness and objective measures [2, 3], other authors confirmed the
validity of questionnaires. For instance, Belanger et al report that “the
association of reported mold and wheeze was confirmed by measured levels
of fungi and wheeze, suggesting that reports of mold were not biased”[4].
The fact that some studies suggest “an almost complete disagreement
between self-reported dampness, visual inspection by a trained
investigator and measurement using an industrial dampmeter” might even
suggest that objective measurements are not completely reliable. As we
reported in our article, although studies that objectively assess exposure
would be desirable, there are problems with accurate air sampling [5]. The
measurements currently used might not accurately represent the variability
of concentration over time, because the measurement periods are too short
and the variability in repeated measures is elevated over a very short
period of time. Thus, both self-report and direct measurement would be
desirable. However, our study focused on the comparison between possible
effects by current or by early exposure and, obviously, early exposure
assessment could only be assessed through the questionnaire.
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, Frencis HC et al. Endotoxin exposure in
asthmatic children and matched healthy controls: results of IPEADAM study.
Indoor Air 2005; 15 suppl 10:25-32.
3. Dales RE, Miller D, Mc Mullen ED. Indoor air quality and health:
validity and determinats of reported home dampness and moulds. Int J
Epidemiol 1997; 26:120-125.
4. Belanger K, Beckett W, Triche E, et al. Symptoms of wheeze and
persistent cough in the first year of life: association with indoor
allergens, air contaminants, and maternal history of asthma. Am J
Epidemiol 2003;158:195-202.
5. Douwes J, Pearce N. Is indoor mold exposure a risk factor for
asthma? Am J Epidemiol 2003;158:203-6.
The interesting results of Delphi study (OEM 2005; 62: 406-413)
underline the increasing importance of a specific training for physicians
involved in the prevention of accidents and other work-related disorders
and diseases. Although EU countries have similar legislation concerning
activities and individual prevention on the workplace, training curricula
for doctors involved in the health activities...
The interesting results of Delphi study (OEM 2005; 62: 406-413)
underline the increasing importance of a specific training for physicians
involved in the prevention of accidents and other work-related disorders
and diseases. Although EU countries have similar legislation concerning
activities and individual prevention on the workplace, training curricula
for doctors involved in the health activities are variable in different
countries. In Italy for instance a legislation approved by the Italian
Parliament in 2001 has extended to specialists in Hygiene and preventive
medicine and in Forensic medicine (“medicina legale”) the licence to
practice health surveillance in the workplace (become “competent” doctor
or “medico competente”), an undertaking so far reserved to specialists in
Occupational medicine.[2,3]
Occupational Medicine training was mainly oriented in the past
decades to clinical occupational medicine only which, though important,
does not give a full response to the needs for expertise in a preventive
workplace-oriented occupational health service, as underlined also in a
recent WHO report.[4]
The post-graduate training for specialists in Hygiene and preventive
medicine is mainly oriented to environmental hygiene and environmental
health, management, communication, health education, epidemiology and
medical statistics.[2]
The curriculum of the specialist in Forensic medicine is oriented to
health legislation, legal obligations of physicians and health personnel,
writing reports about health problems other than more specific training in
forensic medicine.[3]
Although the extension to the two new specialities was not well
accepted by the specialists in Occupational medicine[5], it seems that
the recent results of the Delphi study[1,6], as well as other
recommendations[4,7], stress the importance of the latter two post-
graduate curricula. In fact, according to customer opinions, the four most
important areas of competency of occupational physicians are law, hazards,
fitness and communication. For the training in these competencies the
present curricula in Hygiene and preventive medicine and in Forensic
medicine seem appropriate for the training of the “competent” doctor in
Italy. An additional analysis of the results, which took into
consideration the specific competencies required by the Occupational
physician, show that the activities which obtained the highest scores were
much more present in the curricula of the two post-graduated programmes
(Hygiene and Forensic medicine) introduced in 2001: applying legal and
other ethical requirements for confidentiality (score of 4.48 in Delphi
study); being well informed about acts, regulations, codes of practice
(4.36); identifying the occupational needs (4.25); understanding the
differences between work related and environmental related diseases
(4.11); assessing the work environment and evaluating risks (4.11).
In conclusion the results of Delphi study applied to training
programmes and continuing professional education in Italy indicate that
the most profitable way for the implementation of curricula for
Occupational physicians (“competent” doctors) is the co-operation between
the scientific associations of Occupational medicine, Hygiene and
preventive medicine and Forensic medicine. This in order to adopt common
initiatives to better match the modern training needs of trade unions,
companies and workers and to create in a short time a cadre of
appropriately skilled doctors.
Carlo Signorelli, PhD
Full Professor of Hygiene
University of Parma
Dept. of Public Health
Via Volturno, 39 – 43100 PARMA
References
1. Reetoo KN, Harrington JM, Macdonald EB. Required competencies of
occupational physicians: a Delphi survey of UK customer. Occup Environ Med
2005; 62: 406-413.
2. Carreri V, Signorelli C, Marinelli P, Fara GM, Boccia A. New
opportunities to improve occupational health in Italy. Lancet 2002; 360:
723.
3. Tomassini A. New opportunities to improve occupational health in
Italy. Lancet. 2002 Aug 31;360(9334):723-4.
4. WHO. Global Strategy on Occupational Health for All.
Recommendation of the Second Meeting of the WHO Collaborating Centres in
Occupational Health, Beijing, China, 11-14 October 1994.
5. Manno M, , Mutti A, Apostoli P, Bartolucci B, Franchini I.
Occupational medicine at stake in Italy. Lancet. 2002;359: 1865.
6. Macdonald EB. Ritchie KA, Murrey KJ, Gilmpur WH. Requirements for
occupational training in Europe: a Delphi study. Occup Environ Med 2000;
57: 98-105.
7. Turner S, Hobson J, D’Auria D, Beach J. Continuing professional
development of occupational medicine practitioners: a needs assessment.
Occupational Medicine 2004; 54: 14-20.
I was not against figure 1. Instead, I was concerning the second
scenario in figure 1: people who had respiratory diseases would have a higher
rate of IHD if they kept exposure to dust—there might be an interaction
between respiratory diseases and dust exposure after. In the discussion of
the paper it states that “The direct independent effect of dust exposu...
I was not against figure 1. Instead, I was concerning the second
scenario in figure 1: people who had respiratory diseases would have a higher
rate of IHD if they kept exposure to dust—there might be an interaction
between respiratory diseases and dust exposure after. In the discussion of
the paper it states that “The direct independent effect of dust exposure
on IHD and other CVDs was small”, but many people who had been diagnosis
for respiratory diseases would be removed from the dust exposure job (I am
sorry that I express it as “cohort” in the last letter). Thus most
people who already had been diagnosed for respiratory disease, were not
exposed to dust exposure after they had been diagnosed, even the
“cumulative exposure to dust was considered until the diagnosis date of
ischaemic heart disease (IHD)” .
However as the reply showed that "the exposure data only including
dust exposure after the diagnosis of a respiratory disease," had a small
effect, my question is answered.
I thank the authors so much to give more detail on the information on
smoking. Eletter is a great way to reduce the information lost which is
due to limited space in paper journals. Would it be appropriate to
encourage authors to add more detail on journal websites?
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.
Dear Editor,
This article is presenting both the applied question and the statistical methods used in a very well organised and excellent way. Though, I have some comments on the use of the words risk, effect and predictor as these imply a casual relationship and make us believe that we model the development of neck pain.
The data in the article is longitudinal and in the analysis the authors are using this...
The Editor,
We appreciate Dr. Kromhout’s comments regarding our article “Air samples versus biomarkers for epidemiology”[1] and are pleased that he supports our recommendation that both air samples and biomarkers be collected whenever possible. Kromhout raises three points in his letter. First, he suggests that our conclusion that biomarkers tend to be better surrogates for exposures than air samples might have b...
Dear Editor,
Sir, the recent paper by Lin et al.[1] in the November issue of the journal was a thought provoking piece of work. In their paper the authors try to prove the theoretically derived hypothesis that biomarkers of exposure have smaller variance ratios and would typically provide less biased surrogates of exposure compared to air measurements. Although I entirely agree with the theoretical part of this s...
Dear Editor,
We have read the study on respiratory disease and cardiovascular morbidity by Koskela and coworkers with great interest.[1] They found no obvious effect of direct dust exposure on ischaemic heart disease (IHD) among granite workers and workers in metal industry such as foundry workers and iron foundry workers in Finland. Furthermore, there was a weak association between dust exposure and chronic bronc...
Dear Editor
We read with interest the article by Sorahan et al., “Cancer risks in a historical UK cohort of benzene exposed workers” [1]. We note that the authors showed an increased SMR and SRR for lung cancer among this group. They comment that “there was evidence of increased mortality for lung and lip cancers and for ANLL, and increased morbidity for lung and pleural cancers. There is no reason to suspect that benze...
Dear Editor
We thank Helen C Francis for the interest in our article “Mould/dampness exposure at home is associated with respiratory disorders in Italian children and adolescents: the SIDRIA-2 Study” [1] and we appreciate her comments reported in the letter “The validity of self- reported measures of mould/dampness”, 21 September, 2005. We think it is difficult to compare our findings with those of Tavernier and co...
Dear Editor,
The interesting results of Delphi study (OEM 2005; 62: 406-413) underline the increasing importance of a specific training for physicians involved in the prevention of accidents and other work-related disorders and diseases. Although EU countries have similar legislation concerning activities and individual prevention on the workplace, training curricula for doctors involved in the health activities...
Dear Editor,
I thank the authors for they reply.
I was not against figure 1. Instead, I was concerning the second scenario in figure 1: people who had respiratory diseases would have a higher rate of IHD if they kept exposure to dust—there might be an interaction between respiratory diseases and dust exposure after. In the discussion of the paper it states that “The direct independent effect of dust exposu...
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...
Dear Editor,
“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...
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