In commenting on our paper published recently in OEM,[1] Kromhout
and van Tongeren admonish us for paying insufficient attention to the
earlier literature on occupational pollutant exposures. Whilst no doubt
an element of their criticism is justified, we feel that the exposure
situation for the general public is sufficiently different that it should
not be assumed that findings in the occupational...
In commenting on our paper published recently in OEM,[1] Kromhout
and van Tongeren admonish us for paying insufficient attention to the
earlier literature on occupational pollutant exposures. Whilst no doubt
an element of their criticism is justified, we feel that the exposure
situation for the general public is sufficiently different that it should
not be assumed that findings in the occupational environment can
necessarily be extrapolated to environmental exposures of the general
public. A large component of environmental exposure arises from diffuse
sources and may therefore be very spatially homogeneous at locations such
as peoples’ homes which are often relatively remote from outdoor pollution
sources.
There has been some controversy in the literature regarding the
extent to which measurements at fixed central urban background monitoring
locations can reflect the exposures of large urban populations who spend
much of their time indoors at locations relatively remote from the
monitoring station [e.g. 2,3]. It has been typical to find that for an
individual, daily personal exposures correlate with concentrations at the
monitoring station, whilst if data are pooled from many individuals, the
exposures appear to be uncorrelated with ambient air data.[4,5] This
finding suggests that the diffuse background as represented by the central
urban monitor does account for a substantial proportion of variance in the
exposure of an individual and this conclusion is supportive of causality
in the time series epidemiological studies, which would appear implausible
if the monitoring data were unrelated to human exposures. The finding of
our paper that microenvironment measurements do, in general, well
represent individual personal exposures in that microenvironment
(excepting for the personal cloud of PM10) is far from self-evident from
much of the earlier literature and is a useful addition to knowledge. The
fact that cigarette smokers were outliers in the regression analysis shows
not unexpectedly that they generate strong local concentration gradients
and would therefore need to be treated differently in any modelling of
personal exposures. In the absence of such local sources of pollution,
our study supports the concept that were sufficient microenvironment
measurement data available, it would be perfectly feasible to model
personal exposures with some degree of reliability.
Kromhout and van Tongeren advocate the use of personal exposure
measurements in environmental epidemiological studies. In doing so, they
fail to acknowledge the magnitude of such studies. For example, in the
large North American cohort studies, 8111 subjects were recruited in the
Harvard Six Cities Study and over one million in the American Cancer
Society Study. Were it possible to reconstruct the exposure environments
of those individuals, even in a rather general way from time activity
diaries, a considerable refinement would have been achieved. Even in
panel studies, which typically recruit a far smaller number of
individuals, the subjects are frequently drawn from susceptible groups and
therefore not willing to be encumbered with troublesome and heavy sampling
equipment. It must be remembered that concentrations in environmental
samples are typically orders of magnitude lower than in occupational
samples, therefore requiring higher flow rates (hence bigger pumps) and
longer sampling intervals. In some instances it may be possible to use
passive samplers such as diffusion tubes and badges, but these are
typically rather imprecise and are available only for a very limited range
of pollutants.
In summary, therefore, whilst the measurement of personal exposure in
environmental epidemiology is highly desirable, it is in reality very
unlikely to be practicable in most studies. Thus, our study of the
feasibility of reconstructing exposures from microenvironment data is well
justified and has thrown useful light on the problem, for example, in
illustrating the lower exposures of members of certain of our susceptible
groups.
Roy M. Harrison, Rob P. Kinnersley, Royston G. Lawrence
University of Birmingham
Jon G. Ayres
University of Aberdeen
David Mark
Health & Safety Laboratory
References
(1) R.M. Harrison,
C.A. Thornton, R.G. Lawrence, D. Mark, R.P. Kinnersley and J.G. Ayres. Personal exposure monitoring of particulate matter, nitrogen
dioxide and carbon monoxide, including susceptible groups. Occup. Environ.
Med., 59, 671-679 (2002).
(2) C.H. Linaker, A.J. Chauthan, H.M. Inskip,
S.T. Holgate, D. Coggon. Personal exposures of children to nitrogen dioxide relative to
concentrations in outdoor air. Occup. Environ. Med., 57, 472-176 (2000).
(3) D. Mark, S.L. Upton, C.P. Lyons, R. Appleby, E.J. Dyment, W.D.
Griffith and A.A. Fox. Personal exposure measurements of the general public to atmospheric
particles. Ann. Occup. Hyg., 41, Suppl 1, 700-706 (1997).
(4) N.A.H. Janssen, G. Hoek, B. Brunekreef, H. Harssema, I. Mensink and A.
Zuidhof. Personal sampling of particles in adults: Relation among personal,
indoor and outdoor air concentrations. Am. J. of Epidemiol., 147, 537-547 (1998).
(5) L. Wallace. Correlations of personal exposure to particles with outdoor air
measurements: A review of recent studies. Aerosol Sci. & Technol., 32, 15-25 (2000).
Sorahan and Nichols,[1] writing in this journal, incorrectly understate
the strength of evidence for work-related increased mortality among their
cohort of production workers in the UK flexible polyurethane foam
industry. Their study actually found “some” evidence for a work-related
increase in all-cause mortality, respiratory disease mortality, and lung
cancer mortality in this exposure circumstance,...
Sorahan and Nichols,[1] writing in this journal, incorrectly understate
the strength of evidence for work-related increased mortality among their
cohort of production workers in the UK flexible polyurethane foam
industry. Their study actually found “some” evidence for a work-related
increase in all-cause mortality, respiratory disease mortality, and lung
cancer mortality in this exposure circumstance, especially taking into
account the healthy worker effect.[2] We are concerned to correct this
error, because the UAW represents substantial numbers of workers exposed
to this process, and the UK data provides the first evidence of a
mortality hazard in this industry, in contrast to two previous, perhaps
weaker studies.[3,4]
The authors observed an all-cause SMR among men of 107 (101 to 113),
a respiratory disease SMR of 120 (101 to 141). Elevated mortality of
similar magnitude from these causes was observed among the smaller number
of women, and the SMRs for both genders combined were significantly
elevated. Elevated SMRs for all-cause and respiratory disease mortality
are hardly ever seen in occupational cohorts except for foundry and
asbestos workers. Typically, the SMR for all-cause mortality is about 80,
and SMR for most cancer causes about 90 in the absence of exposure to a
carcinogen at the site.[5] We have observed SMRs for all-cause mortality
as low as 60 in UAW vehicle assembly and stamping cohorts.[6] We are
surprised that these authors mentioned a deficit for all-causes in the
abstract of their previous study of this cohort, but make no mention of
the excess in the present paper.[7]
For lung cancer, the authors noted a significant SMR of 181 (126 to
251) for lung cancer among women. They discount this partly because the
SMR of 107 (90 to 227) among men was only slightly elevated compared to
the general population, without also noting that the combined SMR was 117
(101 to 136) and statistically significantly elevated. The authors also
fail to mention that the SMRs for pancreatic cancer were elevated to a
similar degree in both genders, and the combined SMR was 147 (1.02 to
2.12) and significantly elevated. We believe that consistency in
direction of effect is more important than statistical significance,
especially in view of the healthy worker effect bias against seeing an
effect if it were there.
These findings apply to an exposure circumstance with several suspect
agents. The isocyanates are most prominently associated with non-
malignant respiratory disease. Therefore, the increase in mortality from
this cause is of distinct interest. In addition, pancreatic cancer was
noted in gavage studies of toluene diisocyanate.[8] In our experience, the
most substantial exposure with carcinogenic risk in foam molding is
methylene chloride,[9] although brominated and chlorinated alcohol flame
retardants, and formaldehyde are usually present in foam molding
operations. Catalyst amines may also be absorbed through the skin in
physiologically significant amounts.[10] These multiple exposures, often in
different parts of the process, including off-gassing from stored foam,
undermine the ability to see an effect of isocyanates alone.
We now turn to the exposure response portion of the study. Health
related termination of exposure has previously been noted as an obstacle
to finding an exposure response effect based on duration.[11] Those with
highest exposure to isocyanates would be expected to be sensitized and
migrate into lower exposure jobs; in any event, there were only 19 of 1652
deceased workers with more than 5 years in higher exposed jobs, no lung
cancer victims and only 2 respiratory disease victims. In our view, the
absence of an exposure response relationship in a cohort with such a small
higher exposed group detracts little from our concern for occupational
cause of an observed excess.
More damaging to the evidence of occupational causation is the
absence of a monotonic increasing trend with latency from first exposure:
The general trend of increased risk in exposure strata greater than 10
years latency, clearly significant for all-cause mortality, is not seen in
those with greater than 30 years latency. However, we note that the all
cause and respiratory disease SMRs are at unity or above for this long
latency strata, itself a highly unusual observation. The confidence
intervals overlap between strata, so while there is not a significant
increase, there is no inverse latency response relationship. In addition,
much the largest portion of this long latency group must have come from
two of the eleven plants (Factory 3 and 4 in Table 1) where exposures may
have contrasted to other 9 facilities.
In summary, this study has found a highly unusual and statistically
significant elevation in all cause mortality and respiratory disease
mortality in the cohort as a whole, consistently in both men and women.
For women alone, and men and women combined there was significantly
increased mortality for lung cancer, and for both genders combined,
pancreatic cancer. This is certainly “some evidence” for work related
mortality, respiratory disease mortality, and cancer mortality in the
exposure circumstance of polyurethane foam production. The nature of the
cohort gave little prospect for observing an exposure response
relationship, if it were there.
We also note that, unlike all the other papers in this edition of the
journal, these authors have neglected to acknowledge their funding source.
Reference
(1) T. Sorahan and L. Nichols, "Mortality and cancer morbidity of
production workers in the UK flexible polyurethane foam industry: updated
findings, 1958-98." Occup Environ Med (2002) 59, 751-8.
(2) A. J. McMichael, "Standardized mortality ratios and the "healthy
worker effect": Scratching beneath the surface." J Occup Med (1976) 18,
165-8.
(3) T. M. Schnorr, K. Steenland, G. M. Egeland, M. Boeniger, and D.
Egilman, "Mortality of workers exposed to toluene diisocyanate in the
polyurethane foam industry." Occup Environ Med (1996) 53, 703-7 .
(4) L. Hagmar, H. Welinder, and Z. Mikoczy, "Cancer incidence and
mortality in the Swedish polyurethane foam manufacturing industry." Br J
Ind Med (1993) 50, 537-43.
(5) R. M. Park, N. A. Maizlish, L. Punnett, R. Moure-Eraso, and M. A.
Silverstein, "A comparison of PMRs and SMRs as estimators of occupational
mortality." Epidemiology (1991) 2, 49-59.
(6) R. Park, J. Krebs, and F. Mirer, "Mortality at an automotive
stamping and assembly complex." Am J Ind Med (1994) 26, 449-63.
(7) T. Sorahan and D. Pope, "Mortality and cancer morbidity of
production workers in the United Kingdom flexible polyurethane foam
industry." Br J Ind Med (1993) 50, 528-36.
(8) National Toxicology Program,"TR-251: Toxicology and
Carcinogenesis Studies of Commercial Grade 2,4 (80%)- and 2,6 (20%)-
Toluene Diisocyanate (CAS No. 26471-62-5) in F344/N Rats and B6C3F1 Mice
(Gavage Studies)."; 86
(9) NIEHS, TR-306: Toxicology and Carcinogenesis Studies of
Dichloromethane (Methylene Chloride) (CAS No. 75-09-2) in F344/N Rats and
B6C3F1 Mice (Inhalation Studies), (1986).
(10) E. L. Baker, D. C. Christiani, D. H. Wegman, M. Siroky, C. A.
Niles, and R. G. Feldman, "Follow-up studies of workers with bladder
neuropathy caused by exposure to dimethylaminopropionitrile." Scand J Work
Environ Health (1981) 7 Suppl 4, 54-9.
(11) K. Steenland, J. Deddens, A. Salvan, and L. Stayner, " Negative
bias in exposure-response trends in occupational studies: modeling the
healthy workers survivor effect." Am J Epidemiol (1996) 143, 202-10.
Parodi et al. raised several comments on our cohort mortality study
of petroleum refinery workers in California.[1] Their comments are general
in nature and apply to most, if not all, occupational cohort mortality
investigations in general and refinery studies in particular, including
such studies conducted in the US, the UK, Canada and Italy.[2-7] We have
discussed the same issues in our original...
Parodi et al. raised several comments on our cohort mortality study
of petroleum refinery workers in California.[1] Their comments are general
in nature and apply to most, if not all, occupational cohort mortality
investigations in general and refinery studies in particular, including
such studies conducted in the US, the UK, Canada and Italy.[2-7] We have
discussed the same issues in our original paper. Below we will reiterate
and expand our discussion of these issues in the order raised by Parodi et al.
The first comment raised by Parodi et al. is the potential impact of
the healthy worker effect (HWE) in our study. More specifically, Parodi et al. conjectured that the HWE might have masked an excess of leukemia,
particularly in employees hired after 1949. The HWE is a potential problem
common to all cohort studies that use general populations as the basis for
comparison. All petroleum cohort studies conducted in the US, the UK,
Canada and Italy are equally vulnerable. However, when raising the HWE as
an issue, one must consider the following points. First, it is generally
recognized that the disease most strongly affected by the HWE is
cardiovascular disease and that the HWE has little impact on cancer. This
view is supported by studies from the US, Canada and Europe.[8-11] Second,
the HWE diminishes over time after hire. Monson8 estimated that the HWE
generally lasted about 15 years. In our study, there was no significant
increase of leukemia among employees 20 or 30 years after hire, regardless
of hire date (before or after 1949). Therefore, the lack of a leukemia
excess in our study was not likely due to the HWE. Monson summarized most
sensibly the impact of the HWE as follows: “The healthy worker effect is
relatively weak in comparison to causal excesses that can be detected in
epidemiologic data.”
The second comment raised by Parodi et al. concerns the lack of
exposure information in our study that would have allowed us to classify
workers by exposure and to conduct more detailed exposure-specific
analyses. Again, the lack of detailed exposure information is a general
problem for all retrospective cohort studies, and our study of California
refinery workers is no exception. We acknowledged this limitation in our
original paper. A similar comment regarding the lack of detailed
classification of workers by exposure or job activity was raised
previously concerning the finding of lung cancer in another study of US
petroleum workers, but subsequent detailed analyses by job title revealed
no increase of lung cancer for insulators, pipe fitters, electricians,
boilermakers, or maintenance workers.[12] The most appropriate approach to
deal with specific exposures is to conduct cohort-based or nested case-
control studies. Such nested case-control studies have been conducted
subsequently for a number of cohort studies of petroleum workers in the
US, the UK and Canada.[13-16] Detailed exposure information (including
quantitative estimates) was collected on individual cases and controls in
these investigations. Furthermore, comparisons in these case-control
studies are internal, thus avoiding the HWE. Based on nested case-control
studies, Rosamilia et al. [13] did not find any relation between lung cancer
and asbestos exposure at a US refinery; Wong et al. [14] did not find any
increase of leukemia, kidney cancer or multiple myeloma in relation to
gasoline (hence, benzene) exposure among US petroleum workers; Schnatter et al. [15] did not find any relation between lymphohematopoietic
malignancies and benzene exposure in Canadian petroleum workers; and
Rushton and Romaniuk[16] concluded that there was no evidence of an
association between benzene exposure and lymphoid leukemia, either acute
or chronic, among petroleum workers in the UK. Thus, none of the nested
case-control studies contradicted the findings of the original cohort
studies; nor is it axiomatic that the absence of analyses based on
detailed exposure information automatically implies masked health effects.
Parodi et al. criticized the inclusion of employees hired after 1980
in our study, and argued that the latency of these workers (15 years
maximum) might not have been sufficient, thus “diluting” the risk of
prolonged exposures among those hired in or before 1980. We would like to
point out that, first, our investigation is not merely an academic
exercise but part of an ongoing corporate medical monitoring program that
includes all employees. Second, an analysis stratified by latency was
performed (Table 3 in our original paper). The groups with 20-29 and 30+
years of latency did not include any employees hired after 1980 and,
therefore, could not have been “diluted” by workers hired after 1980.
Third, with regard to prolonged exposures, an analysis stratified by
duration of employment was also performed (Table 2 in our original paper),
and the groups with 15-29 and 30+ years of employment would certainly have
had prolonged exposures and the results would not have been “diluted” by
employees hired after 1980.
With regard to statistical analysis, Parodi et al. questioned our
analysis by period of hire before and after 1949, and suggested cut-points
of 1969 and 1979. We do not understand the basis of their suggestion. We
chose 1949 because of historical exposure patterns. In 1947, the
recommended standard for benzene exposure in the US was reduced from 100
ppm to 50 ppm, which was further reduced to 35 ppm in 1948. Benzene
exposure levels in the petroleum industry were significantly reduced after
1949, thus making 1949 a good surrogate measure for exposure.
Finally, Parodi et al. commented that mortality might not be a good
indicator of cancer risk. This general comment, of course, applies to all
studies based on mortality. In the US there is no national cancer
registry, and it is simply not possible to ascertain cancer incidence in
an historical cohort study of more than 18,000 workers that goes back to
1950. In their comments, Parodi et al. were concerned with exposures to
asbestos and benzene. The cancers related to these exposures are lung
cancer, malignant mesothelioma and acute myeloid leukemia. These
particular cancers have relatively poor prognosis, particularly in the
past, and mortality may not be an unreasonable outcome measure. Parodi et al. also commented on the diagnostic accuracy of death certificates.
Again, this comment applies to all studies based on mortality. It should
be noted that diagnostic accuracy varies by disease. For example, lung
cancers are seldom misdiagnosed. Although some diagnoses on death
certificates may not be as accurate as those based on detailed medical
records, relying on death certificates in both the study and reference
populations ensures comparability.
Furthermore, our interpretation of the results was based on not only
what we observed, but also the findings of related studies. For example,
for non-Hodgkin’s lymphoma (NHL) and multiple myeloma (MM), in addition to
our results, we also relied on hospital-based case-controls studies which
were included in previous reviews cited in our paper.[17,18] The diagnoses
in these hospital-based case-control studies were based on detailed
clinical, laboratory and pathological findings. The conclusion from these
hospital-based case-control studies is that there is no relation between
benzene exposure and NHL or MM. Therefore, our conclusion of MM and NHL
based on our study is consistent with other studies in which diagnostic
accuracy is not an issue.
In their conclusion, Parodi et al. cautioned that results should not
be ignored simply on the basis of the lack of statistical significance and
suggested nested case-control studies be conducted for further
investigation. We concur on these two points. In discussing the results of
our study, we did not ignore any findings simply because they were not
statistically significant. We fully recognized that a result might not be
statistically significant because the statistical power of an individual
study might not be adequate and that the result must be interpreted in
conjunction with other similar studies. As one of the objectives stated in
our original paper, we assessed our findings (statistically significant or
otherwise) in the context of results of other petroleum studies. To take
all studies into consideration, we also relied on several reviews and meta
-analyses of studies of petroleum workers around the world.[12,17-19] For
example, based on a combined database of more than 350,000 petroleum
workers in the US, the UK, Canada, Australia, Finland Sweden and Italy,
Wong and Raabe [12] reported that consistently not a single study
demonstrated an increase of lung cancer and the summary lung cancer
standardized mortality ratio was 0.81, with a 95% confidence interval of
0.79-0.83 (based on 5695 deaths). As to the suggestion of further
investigations using nested case-control studies, such detailed case-
control studies have been conducted among petroleum workers in the US, the
UK and Canada.[13-16] As discussed above, none of these nested case-control
studies contradicted the findings of the original cohort studies.
Therefore, while Parodi et al. have raised several limitations common
to occupational retrospective cohort studies, we believe that we already
have discussed these issues in our original paper and that we have not
over-interpreted our data.
Kenneth Satin
Willim Bailey
Kimberly L Newton
Anita Y Ross
ChevronTexaco
PO Box 1627, Richmond, CA 94802-0627, USA
Otto Wong
Applied Health Sciences, Inc.
PO Box 2078, San Mateo, CA 94401, USA
References
(1) Satin KP, Bailey WJ, Newton KL, Ross AY, Wong O. Updated
epidemiological study of workers at two California petroleum refineries,
1950-95. Occup Environ Med 2002;59:248-256.
(2) Wong O, Harris F, Smith TJ. Health effects of gasoline exposure.
II. Mortality patterns of distribution workers in the United States.
Environ Health Perspect 1993;101(suppl. 6):63-76.
(3) Raabe GK, Collingwood KW, and Wong O. An updated mortality study
of workers at a petroleum refinery in Beaumont, Texas. Am J Ind Med
1998;33:61-81.
(4) Schnatter AR, Katz AM, Nicolich MJ, Thériault G. A retrospective
mortality study among Canadian petroleum marketing and distribution
workers. Environ Health Perspect 1993;101(suppl. 6);85-99.
(5) Rushton L. Further follow-up of mortality in a United Kingdom oil
refinery cohort. Occup and Environ Med 1993;50:549-60.
(6) Gennaro V, Ceppi M, Boffetta P, Fontana V, Perrotta A. Pleural
mesothelioma and asbestos exposure among Italian oil refinery workers.
Scand J Work Environ Health 1994;20:213-215.
(7) Consonni D, Pesatori AC, Tironi A, Bernucci I, Zocchetti C,
Bertazzi A. Mortality study in an Italian oil refinery: extension of the
follow-up. Am J Ind Med 1999;35:287-294.
(8) Monson RR. Observations on the healthy worker effect. J Occup Med
1986;28:425-433.
(9) Enterline PE. Not uniformly true for each cause of death. J Occup
Med 1975;17:127-128.
(10) Howe GR, Chiarelli AM, Lindsay JP. Components and modifiers of
the healthy worker effect: evidence from three occupational cohorts and
implications for industrial compensation. Am J Epidemiol 1988;128:1364-
1375.
(11) Gridley G. Nyren O, Dosemeci M, Moradi T, Adami HO, Carroll L,
Zahm SH. Is there a healthy worker effect for cancer incidence among women
in Sweden? Am J Ind Med 1999;36:193-199.
(12) Wong O and Raabe GK. A critical review of cancer epidemiology in
the petroleum industry, with a meta-analysis of a combined database of
more than 350,000 workers. Reg Toxicol Pharmacol 2000;32:78-98.
(13) Rosamilia K, Wong O, Raabe GK. A case-control study of lung
cancer among refinery workers. J Occup Environ Med 1999;41:1091-1103.
(14) Wong O, Trent L, Harris F. Nested case-control study of
leukemia, multiple myeloma, and kidney cancer in a cohort of petroleum
workers exposed to gasoline. Occup Environ Med 1999;56:217-221.
(15) Schnatter AR, Armstrong TW, Nicolich MJ, Thompson FS, Katz AM,
Huebner WW, Pearlman ED. Lymphohematopoietic malignancies and quantitative
estimates of benzene exposure in Canadian petroleum distribution workers.
Occup Environ Med 1996;53:773-781.
(16) Ruston L, Romaniuk H. A case-control study to investigate the
risk of leukemia associated with exposure to benzene in petroleum
marketing and distribution workers in the United Kingdom. Occup Environ
Med 1997;54:152-166.
(17) Wong O, Raabe GK. Non-Hodgkin’s lymphoma and exposure to benzene
in a multi-national cohort of more than 308,000 petroleum workers, 1937-
1996. J Occup Environ Med 2000;42:554-568.
(18) Wong O, Raabe GK. Multiple myeloma and benzene exposure in a
multi-national cohort of more than 250,000 petroleum workers. Reg Toxicol
Pharmacol 1997;26:188-199.
(19) Wong O, Raabe GK. Cell-type specific leukemia analyses in a
combined cohort of more than 208,000 petroleum workers in the United
States and the United Kingdom, 1937-1989. Reg Toxicol Pharmacol 1995;21:307-321.
We would like to comment on the paper by Satin et al, [1] which reports an
update of a mortality investigation on two cohorts of petroleum refinery
workers. The Authors claim that one of the major aims of their study was
the assessment of “health risks relative to more contemporary levels of
exposure and work environments”. Nevertheless, they explicitly admit that
a previous investigation in such...
We would like to comment on the paper by Satin et al, [1] which reports an
update of a mortality investigation on two cohorts of petroleum refinery
workers. The Authors claim that one of the major aims of their study was
the assessment of “health risks relative to more contemporary levels of
exposure and work environments”. Nevertheless, they explicitly admit that
a previous investigation in such cohorts,[2] using the population of
California as referent, found a strong “healthy worker effect” (i.e. a
significantly lower than expected mortality risk from cardiovascular
disease and lung cancer). In our opinion, this observation, along with
some other drawbacks, only in part expressly acknowledged in the paper,
might have biased most of the results obtained, leading the Authors to
draw unreliable conclusions. We shall discuss this issue in detail,
illustrating the main possible biases and how we believe they should have
been interpreted.
Comparison bias
Exposure effects should be assessed in cohort studies by comparing the
exposed cohorts with at least an unexposed one, as similar as possible in
all relevant aspects.[3]
The new results by Satin et al. have confirmed the occurrence of the
“healthy worker effect” observed in the previous follow up. Such a finding
may indicate a comparison bias concealing the associations, if any,
between exposure and health risks.[2] In fact, occupational cohorts may
differ from the general population in many features that have been
associated with various risk factors, including socio-economic status and
personal habits.[3] The presence of a comparison bias, at least in the
Richmond refinery cohort, seems to be suggested by the risk for leukaemia
in the sub-group with the shortest duration of employment (<5 years),
which is more than 4 times lower than the referent population (and nearly
7 times lower than those of workers who worked the longest, i.e. more than
30 years). Finally, the lack of data on smoking, whose differential
distribution is among the main factors known to be responsible for the
“healthy worker effect”, should have suggested a more cautious
interpretation of the results of analyses about diseases associated with
such a risk factor, especially lung cancer. Owing to the quality of the
data analysed, most of these limits are unavoidable. However, in our
opinion, the Authors should have taken them into account in discussing
their results. For instance, the low leukaemia risk observed, in
particular, for workers hired after 1949, should not have been considered
as evidence of a lack of effect of quite low doses of benzene.
Dilution effect
Petrochemical workers are likely to experience different kinds and levels
of exposure by job category. As a consequence, results from an analysis
carried out by pooling together different exposure categories may be
affected by a dilution effect, i.e. an underestimation of the true
mortality risk associated to exposure.[3] For example, Gennaro et al.[4-
6] highlighted an excess of lung cancer risk among petroleum workers
exposed to asbestos in an Italian refinery, which became evident only by
using an unexposed job category as an internal referent group. In this
investigation, the most heavily exposed group (maintenance workers) was
38% of the whole cohort of employees, very similar to the proportion (36%)
reported in a previous study on ten US refineries.[7] Furthermore, white
collar workers constituted 22% and 21% of the workforce among the Italian
and US refineries, respectively, suggesting that the composition of this
kind of cohort tends to be similar, at least in Western countries.
Unfortunately, the quality of the data in their possession prevented the
Authors from carrying out risk analyses by job category, and they did not
discuss about the possibility that the inclusion, if it occurred, of a
notable proportion of workers scarcely or not at all exposed may have
caused a significant lowering of the estimated risks.[3]
Moreover, the inclusion in the present analysis of workers employed after
31 December 1980, thus inflating the at risk population estimates (person-
years), could have further contributed to diluting the possible risks, in
addition to preventing a precise comparison with the previous update. In
fact, a long lag-time is expected between exposure and disease occurrence
for most of the cancer sites considered. The mean time of follow up was
roughly 33 years for workers hired before 1949 and only 23 for those hired
after 1949, but the Authors have not provided any information about the
group employed since 1981, making it impossible to estimate the true risks
associated with prolonged exposures. Comparing the paper by Satin et al.
to the previous follow up,[2] the number of workers enrolled after 1980
could amount to 3600 (i.e. 31% of the subjects hired after 1949) and the
corresponding follow-up ranges from 1 to 15 years. However, these data do
not allow the calculation of either the corresponding person-years at risk
or the number of deaths occurred.
Statistical analysis
The Authors have indirectly evaluated the effect of exposures using the
period of hiring (before vs. after 1949) and the number of years worked as
factors. Due to the lack of more precise measures of the polluting
concentrations, such substitute variables are of course necessary, even
though in our opinion, the analysis for another cut-off after 1949 (e.g.
1969 or 1979) might have yielded some additional information about the
variation of such risks over time. Furthermore, a possible confounding
effect between the period of hiring and the other variables (e.g. length
of exposure and latency) should have been taken into account, for
instance, either by applying a multivariable statistical model, such as
the Poisson regression, or by stratified analysis.[8]
Insensitive indicators
Mortality rates may be poor indicators of cancer risk for disease sites
with a good prognosis, for example, leukaemia and larynx cancer.[3] For
this reason, comparisons based on mortality rates might be affected by
lack of statistical power. Moreover, the Authors admit that the potential
inaccuracy of diagnostic information from death certificates may have
caused misclassification between asbestosis and pneumoconiosis. However,
this inaccuracy might have also affected the risk estimates for specific
leukaemia types. While the Authors do not report any results for
unspecified leukaemia, an other investigation on petrochemical workers [9]
found surprisingly much higher risks for both “acute unspecified” and
“cell type unspecified” leukaemia than those for each specified cell type,
including acute myeloid (AML). In our view, when data come from death
certificates, risk estimates for different leukaemia cell types must be
interpreted with extreme care. In particular, the Author’s claim that “the
lack of any increase in AML argues against benzene’s role in the
increase of MM and NHL found here” is not justified.
Conclusion
Cohort studies based on mortality data and not including an internal group
as a control may be affected by several biases. For this reason, the
estimates of association between exposure to toxic chemicals and health
risks obtained by these studies should be considered with caution.
Moreover, the observed excess of risk, if any, should not be ignored
simply on the basis of the lack of statistical significance. The need for
further investigations for a better evaluation of such risk, for instance,
through nested case-control studies, should be always suggested.
References
(1) Satin KP, Bailey WJ, Newton KL, Ross AY, Wong O. Updated
epidemiological study of workers at two California petroleum refineries,
1950-95. Occup Environ Med 2002;59:248-56.
(2) Dagg TG, Satin KP, Bailey WJ, et al. An updated cause specific
mortality study of petroleum refinery workers. Br J Ind Med 1992;49:203-
12.
(3) Hernberg S. “Negative” results in cohort studies – How to recognize
fallacies. Scand J Work Environ Health, 1981;7:121-126.
(4) Gennaro V, Finkelstein MM, Ceppi M, Fontana V, Montanaro F, Perrotta A,
Puntoni R, Silvano S. Mesothelioma and lung tumors attributable to
asbestos among petroleum workers. Am J Ind Med 2000,37:275-82.
(5) Gennaro V, Montanaro F, Ceppi M, Fontana V, Perrotta A, Puntoni R,
Finkelstein MM, Silvano S. RE: Mesothelioma and lung tumors attributable
to asbestos among petroleum workers. Am J Ind Med 2000. 37:275-282. I.
Reply to Tsai et al.'s Letter to the Editor and new evidencies (letter).
Am J Ind Med 2001; 39(5): 517-521.
(6) Gennaro V, Montanaro F, Ceppi M, Fontana V, Perrotta A, Puntoni R,
Finkelstein MM, Silvano S. RE: Mesothelioma and lung tumors attributable
to asbestos among petroleum workers. Am J Ind Med 2000. 37:275-282.
II. Reply to Bailey's Letter to the Editor (letter). Am J Ind Med 2001;
39(5): 522-523.
(7) Nelson NA, Barker DM, Van Peenen PFD, Blanchard AG. Determining
exposure categories for a refinery retrospective cohort mortality study.
Am Ind Hyg Assoc J 1985; 46: 653-57.
(8) Sahai H, Khurshid A (eds). Statistics in Epidemiology – Methods,
Techniques, and Applications. CRC Press, New York, 1996.
(9) Divine BJ, Hartman CM, Wendt JK. Update of the Texaco mortality study
1947-93: part II. Analyses of specific causes of death for white men
employed in refining, research, and petrochemicals. Occup Environ Med
1999,56; 174-80.
The paper by Harrison et al.[1] and the accompanying editorial by
Cherrie [2] address the important issue of personal exposure assessment (of
air pollutants) in environmental epidemiology. After reading both papers
we would like to make some comments with regard to the design, conduct and
statistical analysis of the study by Harrison et al. and at the same time
answer the question raised by...
The paper by Harrison et al.[1] and the accompanying editorial by
Cherrie [2] address the important issue of personal exposure assessment (of
air pollutants) in environmental epidemiology. After reading both papers
we would like to make some comments with regard to the design, conduct and
statistical analysis of the study by Harrison et al. and at the same time
answer the question raised by Cherrie in his editorial.
Coming from the occupational exposure assessment arena it is
interesting to see that our environmental colleagues are still relying on
to a large extent on static (micro-environmental) sampling and even rely
on shadowing to represent personal exposure. The latter brought back
memories of old occupational hygiene textbooks with pictures of
technicians standing with a sampling probe in the breathing zone of a
worker (clearly hindered while carrying out his work task). It is
interesting to note that Dr Cherrie´s very relevant earlier work [3] on
whether wearing sampling pumps affects exposure (it hardly did) was not
mentioned in both papers.
The paper by Harrison et al.[1] clearly states as one of its goals to
answer the question "Does modelling through the use of microenvironment
measurements and activity diaries produce reliable estimates of personal
exposure to air pollutants". However in the only setting where personal
exposures were actually measured (Phase 1, volunteers; with regard to
Phase 2 we do not think that shadowing results can be seen as equivalent
to personal measured exposure) it is hard to grasp from both Figure 1 and
Table 2 which exposure was actually modelled (1-hour averages, 2-3 day
averages) and how (a formula was only provided for measurements within the
susceptible groups).
When comparing direct personal measurements for CO and PM10 with the
modelled results, the authors exclude all data which are not directly
comparable, i.e. when the volunteer spent most of their time out of house,
and all the data for smokers. It is therefore not surprising that good
correlations were found between personal and static measurement results.
Why were smokers excluded? Was their measured CO exposure representing a
different kind of CO leading to a different health effect? We know that
excluding smokers or people with unventilated gas heaters is common
practice in the statistical analyses of environmental exposures, but this
would only make sense if we were expecting different risks from the same
exposure originating from different sources.
In Figure 1 the authors present 120 comparable data points for 11
individuals and given the repeated nature of the sampling these data
points cannot be seen as statistically independent. Putting a simple
regression line through these points is therefore not correct and
application of a mixed model would have been more appropriate. Besides
that, when estimating environmental exposure for instance for a panel
study, we are interested in the full range of exposures both in the
temporal and spatial sense (not only for the room with the static
sampler). However, Harrison et al. conclude, "...modelled personal exposure
is unable to reflect the variability of measured personal exposures
occasioned by the spread of concentrations within given
microenvironments."
Both Cherrie and Harrison et al. claim that micro-environmental
sampling would be a good alternative for direct personal exposure
measurements that supposedly are "costly and time consuming". However, the
costs for sampling micro environments in a general population study will
be far greater if we want to measure all the micro environments people end
up in (for instance in Table 1 seven environments are indicated and most
of them will most likely be different for each study participant). In
addition, it will be practically impossible to measure some of these
environments as the authors point out. In their study, it was not possible
to collect data for all appropriate microenvironments even for a
comparatively small number of subjects.
Recently, a very insightful paper was presented at the X2001
conference in Gothenburg. Seixas et al.[4] showed that in a study to assess
noise exposure, a task-based methodology (analogous to micro-environmental
sampling in occupational exposure assessment) could only account for 30%
of variability in daily exposures. They even considered this estimate
somewhat optimistic since their estimated noise exposures were derived
from the same data on which the daily average exposures were estimated. In
addition they clearly pointed out that using simple task-based averages
that artificially compress exposure variability resulted in a very
substantial negative bias in the estimated daily exposure.
In our opinion, we should aim to collect personal exposure
measurements when estimating exposure for epidemiological studies. We
agree that smaller and lighter sampling instruments will need to be
developed, as was suggested by Cherrie in his editorial. Recent studies in
both the occupational and environmental arena have shown that study
subjects are capable to carry out personal measurements themselves (and by
doing so, cutting out the costs of the technician).[5-9] In all these
studies but one [7] far more than 100 personal measurements were generated,
which shows that studies of this size are not exceptional as was suggested
in the editorial by Cherrie.
The question that was raised by Cherrie "How important is personal
exposure assessment in the epidemiology of air pollution?" can only be
answered with a firm "Very important", if we want to capture the full
range of personal exposures experienced in the general environment. In
addition, given the relatively low concentrations in the general
environment we will need to measure these accurately. Micro-environmental
monitoring and consequent modelling based on diaries will not provide
sufficient resolution and accuracy.
Hans Kromhout1,2 Martie van Tongeren3
1. Environmental and Occupational Health Division, Institute for Risk
Assessment Sciences, Utrecht University, PO Box 80176, 3508 TD Utrecht,
The Netherlands
2. Research Unit Respiratory and Environmental Health, Municipal
Institute of Medical Research, Barcelona, Spain
3. Centre for Occupational and Environmental Health, School of
Epidemiology and Health Sciences, The University of Manchester,
Manchester, United Kingdom
References
(1) Harrison RM, Thornton CA, Lawrence RG, et al. Personal exposure
monitoring of particulate matter, nitrogen dioxide and carbon monoxide,
including susceptible groups. Occup Environ Med 2002;59: 671–9.
(2) Cherrie JW. How important is personal exposure assessment in the
epidemiology of air pollutants? Occup Environ Med 2002;59:653-54.
(3) Cherrie JW, Lynch G, Bord BS, Heathfield P, Cowie H, Robertson A.
Does the wearing of sampling pumps affect exposure? Ann Occup Hyg
1994;38:827-38.
(4) Seixas N, Sheppard L, Neitzel R. Comparison of task-based and full
-shift strategies for noise exposure assessment in the construction
industry. Arbete och Hälsa 2001; 10:51-3.
(5) Kromhout H, Loomis DP, Mihlan GJ, Peipins LA, Kleckner RC, Iriye
R, Savitz DA. Assessment and grouping of occupational magnetic field
exposure in five electric utility companies. Scand J Work Environ Health
1995;21:43-50.
(6) Egeghy PP, Tornero-Velez R, Rappaport SM. Environmental and
biological monitoring of benzene during self-service automobile refueling.
Environ Health Perspect 2000;108:1195-202.
(7) Tielemans E, Heederik D, Burdorf A, Vermeulen R, Veulemans H,
Kromhout H, Hartog K. Assessment of occupational exposures in a general
population: comparison of different methods. Occup Environ Med 1999;56:145
-51.
(8) Rijnders E, Janssen NAH, Vliet PHN van, B. Brunekreef. Personal
and outdoor nitrogen dioxide concentrations in relation to degree of
urbanization and traffic density. Environ Health Perspect 2001;109:411-7.
(9) Liljelind IE, Rappaport SM, Levin JO, et al. Comparison of self-
assessment and expert assessment of occupational exposure to chemicals.
Scand J Work Environ Health 2001;27:311–7.
We read with interest the article by Hoogendoorn et al. (2002) who examined the use of different approaches to analysing data from their prospective cohort study of work-related exposures and the future onset of low back pain.[1]
Exposures and outcomes are time dependent factors as they are subject to change over time. The strength of the relationship depends on the assumptions of time depend...
We read with interest the article by Hoogendoorn et al. (2002) who examined the use of different approaches to analysing data from their prospective cohort study of work-related exposures and the future onset of low back pain.[1]
Exposures and outcomes are time dependent factors as they are subject to change over time. The strength of the relationship depends on the assumptions of time dependence (or independence) of exposures and outcomes. The effects of these assumptions can be investigated by adopting different modelling approaches to studies that have collected repeated measures of exposure and outcome data over time.
Hoogendoorn et al. (2002) have adopted such an approach in their study of work-related risk factors for low back pain. Information on work-related physical and psychosocial factors and low back pain outcome was collected at baseline and in three annual follow-ups. They demonstrated an increased risk of low back pain for work-related mechanical factors, when using two different generalised estimating equation (GEE) models compared to the standard logistic regression approach.[1] Conversely, for work-related psychosocial factors the association with low back pain was weaker when the GEE method was employed. Such an approach is enlightening and we agree that it is important to explore such analytical techniques in the investigation of work-related risk factors and musculoskeletal symptoms. Therefore further exploitation of this method of analysis seems appropriate.
We have recently conducted a prospective study of new onset low back pain in 1081 newly employed workers from twelve occupational settings.[2] We examined newly employed workers since studies conducted in well established work forces may be influenced by the healthy worker effect, whereby workers may have changed their job or certain aspects of their job as a result of musculoskeletal pain. In brief, at baseline subjects completed a questionnaire, including an assessment of pain status. A pre-shaded manikin was used to enquire about low back pain, defined as pain between the 12th rib and the gluteal folds, lasting at least 24 hours in the past month. Individuals free from low back pain at baseline were identified and followed up at 12 and 24 months. The detailed questionnaire also gathered information on a number of work-related mechanical and psychosocial exposures.
The models used for analysis were identical to those used by Hoogendoorn et al. (2002). The standard logistic regression model was used to examine the relationship between exposures and new onset low back pain at 12 or 24 months, in those free from low back pain at baseline. GEE models are used to analyse repeated measures data, by taking the within subject correlation into account, and providing a summary estimate over time. In GEE Model 1, the relationship between baseline exposures and new onset low back pain at 12 and 24 months was examined. In GEE Model 2 that relationship was examined for baseline exposures and new onset low back pain at 12 months, and 12 month exposures and new onset low back pain at 24 months.
The two models in which risk factors were assumed to be time independent (standard logistic regression and GEE Model 1) produced similar point estimates for developing new onset low back (Table 1 Part i, Part ii and Table 2), with narrower 95% confidence intervals for GEE. Model 1. In GEE Model 2, where risk factors are assumed to be time dependent, differences were noted for only a small number of variables (carrying on one shoulder, lifting at or above shoulder level, general health questionnaire). In addition, the 95% confidence intervals were narrower than those from the standard logistic regression and GEE Model 1. However, there was no consistent pattern of attenuation or growth noted in either the mechanical or psychosocial risk factors examined. (Table 1 Part i, Part ii and Table 2).
In summary, we agree that it is important to investigate different statistical techniques in an attempt to determine what effect the assumptions of time dependence (or independence) have on predictors of musculoskeletal pain. However, unlike the study by Hoogendoorn et al. (2002), our data show that the choice of model has relatively little influence on the magnitude of the results, although GEEs give more accurate estimates.
References
(1) Hoogendoorn WE, Bongers PM, de Vet HC, Twisk JW, van Mechelen W, Bouter LM. Comparison of two different approaches for the analysis of data from a prospective cohort study: an application to work related risk factors for low back pain. Occup Environ Med 2002;59:459-465.
(2) Nahit ES, Macfarlane GJ, Pritchard CM, Cherry NM, Silman AJ. Short term influence of mechanical factors on regional musculoskeletal pain: a study of new workers from 12 occupational groups. Occup Environ Med 2001;58:374-381.
The article by Harrison and colleagues’[1] reports on a relationship
between personal and static microenvironment air sampling for carbon
monoxide and nitrogen dioxide and for PM10 which include the addition "of
a personal cloud increment." Static sampling is also commonly referred to
as area or stationary sampling.[2,3] These relationships are important
because static sampling is more easily achieved th...
The article by Harrison and colleagues’[1] reports on a relationship
between personal and static microenvironment air sampling for carbon
monoxide and nitrogen dioxide and for PM10 which include the addition "of
a personal cloud increment." Static sampling is also commonly referred to
as area or stationary sampling.[2,3] These relationships are important
because static sampling is more easily achieved than personal measurements
and is generally less costly. To achieve a relationship for personal and
static sampling they must be collected from the same pollutant population.[4-7] Thus, in establishing a microenvironment or personal cloud
increment, there must be a relationship within the sampling location for
the pollutant.
Previous occupational studies have noted no relationship[2,4,8-11]
and a relationship[12,13] between personal and static sample
measurements. As mentioned by Harrison et al., personal samples are
generally higher in concentration than static samples because of people
being closer to the source and spending more time within the source
location, or in the emission pathway.[4,14] When static samplers are
placed at the source location or emission pathway they are similar to the
values reported for personal samples,[2,3] and in some incidents may
exhibit a higher concentration.[4,13,15]
The relationship reported by Harrison et al., for CO and NO2 is
likely a result of these pollutants being a gas, their ability to diffuse,
low reactivity, and similarly in concentration between indoor and outdoor
environments. A personal cloud factor must be incorporated into the PM10
measurement because of greater variability of concentration from location
to location.[16] A microenvironment represents a similar location and
the personal cloud is a correction factor extrapolating for the static
exposure to personal measurements. It must be noted that this adds a
degree of uncertainty in extrapolating exposure from one sampling method
to the other. Even though static samples may be reported as similar, they
will ultimately exhibit a lower concentration than personal measurements.
Harrison et al, provided summation of their data in the form of
arithmetic mean (AM) and standard deviation. When data from Tables 2 and
3 were evaluated for form of distribution, using the Shapiro-Wilk test,[17] most exhibited a non-normal distribution (Table). However, due to
the small number of samples in Harrison’s data the actual form of
distribution cannot be determined. It is suggest[2,18] that the
logarithmic form best represents airborne pollutants, including Harrison’s
data. When providing pollutant data, it has been suggested to include
summary statistics that representative it’s form of distribution.[2]
Data should be shown as AM, standard deviation, range, geometric mean, and
geometric standard deviation (GSD).[2,12] It has been suggested[19,20]
that health effects from exposure are more closely related to AM values,
especially for those that are chronic in nature, making AM an important
summary value to report. Reporting all summary statistics will allow
future investigators to select summary data most relevant to their
purpose.
Tables: Form of
distribution for data reported in Harrison et al., Tables 2 and 3
Table 2
Non-transformed
Transformed+
Nitrogen
dioxide
Normal
Normal
Carbon monoxide
Not
normal at 5% or 1%
Not
normal at 5% or 1%
PM10
Not
normal at 5% or 1%
Not
normal at 5% or 1%
Table 3
Non-transformed
Transformed+
Nitrogen
dioxide
Normal
Normal
Carbon monoxide
Not
normal at 5 or 1%
Not
normal at 5%, normal at 1%
PM10
Not
normal at 5% or 1%
Not
normal at 5% or 1%
+ transformation was performed using natural logs
Since many environmental pollutants are distributed throughout a
location, like homes, modeling will prove useful in establishing a
relationship between personal and static samples. However, this
relationship may not only depend on sampling locations and emission
pathways, but the actual pollutant as well.[6]
Variability among samples must also be considered when predicting
exposure levels. Most sample populations exhibit a GSD (day-to-day
variability) of 2.0 to 3.0.[2] The probability of samples with this
variability being “related” is about 28% to 17%.[21] The GSD for the
data reported by Harrison et al, ranged from 1.4 to 2.6. Thus, sample
variability raises issues with the predictability of accuracy in exposure
estimation.[21] This variability may also skew modelling as well
resulting in fallacious interpretations; although as mentioned in Harrison
et al, when the population sample becomes larger or uses pooled data
these influences may become diminished.
Historically, most inferred that there is no relationship between
personal and static exposures,[2-4,6,9-11] while studies such as that
provided by Harrison et al, question this concept. Establishment of a
relationship between these two sampling methods will allow incorporation
of additional data into occupational, environmental and epidemiological
studies,[16] although caution must be applied in interpreting any
relationship based on previous findings.[2,4] Thus, care must be
exercised when evaluating studies that solely use static sampling as the
method of estimating personal exposure.[7]
References
(1) Harrison RM, Thornton CA, Lawrence RG, Mark D, Kinneisley RP,
Ayres JG. Personal exposure monitoring of particulate matter, nitrogen
dioxide, and carbon monoxide, including susceptible groups. Occp Environ
Med 2002; 59:671-9.
(2) Lange JH. A statistical evaluation of asbestos air
concentrations. Indoor-Built Environ 1999; 8:293-303.
(3) Corn M. Assessment and control of environmental exposure. J
Allergy Clin Immunol 1983; 72:231-241.
(4) Lange JH, Kuhn BD, Thomulka KW, Sites SLM. A study of matched
area and personal airborne asbestos samples: evaluation for relationship
and distribution. Indoor and Built Environ 2000; 9:192-200.
(5) Esmen NA, Hall TA. Theoretical investigation of the
interrelationship between stationary and personal sampling in exposure
estimation. Appl Occup Environ Hyg 2000; 15:114-119
(6) Liu LJS, Koutrakis P, Suh HH, Mulik JD, Burton RM. Use of personal
measurements for ozone exposure assessment: a pilot-study. Environ Health
Perspectives 1993; 101:318-324.
(7) Edwards RD, Jurvelin J, Koistinen K, Saarela K, Jantunen M. VOC
source identification from personal and residential indoor, outdoor and
workplace microenvironmental samples in EXPOLIS-Helsinki, Findland.
Atmospheric Environ 2001; 35:4829-4841.
(8) Lange JH, Thomulka KW. Air sampling during asbestos abatement of
floor tile and mastic. Bull Environ Cont Tox 2000; 64:497-501
(9) Linch AL, Weist EG, Carter MD Evaluation of tetraethyl lead
exposure by personal monitoring surveys. Am Ind Hyg Assoc J 1970; 31:170-
179.
(10) Stevens DC. The particle size and mean concentration of
radioactive aerosols measured by personal and static air samples. Ann
Occup Hyg 1969; 12:33-40.
(11) Linch AL, Pfaff HV. Carbon monoxide: evaluation of exposure
potential by personal monitor surveys. Am Ind Hyg Assoc J 1971; 32:745-
752.
(12) Breslin AJ, Ong L, Glauberman H, George AC, LeClare P. The
accuracy of dust exposure estimates obtained from conventional air
sampling. Am J Ind Hyg Assoc J 1967; 28:56-61.
(13) Lange JH, Lange PR, Reinhard TK, Thomulka KW. A study of
personal and area airborne asbestos concentrations during asbestos
abatement: a statistical evaluation of fibre concentration data. Ann Occup
Hyg 1996; 40:449-466
(14) Leung P-L, Harrison RM. Evaluation of personal exposure to
monoaromatic hydrocarbons. Occup Environ Med 1998; 55:249-257.
(15) Lange JH, Thomulka KW. Airborne exposure concentration during
asbestos abatement of ceiling and wall plaster. Bull Environ Cont Tox
2002; 69:712-718.
(16) Cherrie JW. How important is personal exposure assessment in
the epidemiology of air pollutants? Occup Environ Med 2002; 59:653-654.
(17) Shapiro SS, Wilk MB. An analysis of variance test for
normality. Biometrika 1965;52:591-611.
(18) Esmen NA, Hammad Y. Log-normality of environmental sampling
data. J Environ Sci Hlth 1977; A12:29-41.
(19) Seixas NS, Robins TG, Moulton LH. Use of geometric and
arithmetic mean exposures in occupational epidemiology. Am J Ind Med 1998;
14:465-477.
(20) Armstrong BG. Confidence intervals for arithmetic means of
lognormality distribution exposures. Am Ind Hyg Assoc J 1992; 53:481-485.
(21) Leidel NA, Busch KA, Lynch JR Occupational exposure sampling strategy manual. DEHW (NIOSH) Publication Number 77-173, National
Technical Information Service Number PB-274-792. Cincinnati, Ohio: National Institute for
Occupational Safety and Health, 1977.
Dear Editor
In commenting on our paper published recently in OEM,[1] Kromhout and van Tongeren admonish us for paying insufficient attention to the earlier literature on occupational pollutant exposures. Whilst no doubt an element of their criticism is justified, we feel that the exposure situation for the general public is sufficiently different that it should not be assumed that findings in the occupational...
Dear Editor
Sorahan and Nichols,[1] writing in this journal, incorrectly understate the strength of evidence for work-related increased mortality among their cohort of production workers in the UK flexible polyurethane foam industry. Their study actually found “some” evidence for a work-related increase in all-cause mortality, respiratory disease mortality, and lung cancer mortality in this exposure circumstance,...
Dear Editor
Parodi et al. raised several comments on our cohort mortality study of petroleum refinery workers in California.[1] Their comments are general in nature and apply to most, if not all, occupational cohort mortality investigations in general and refinery studies in particular, including such studies conducted in the US, the UK, Canada and Italy.[2-7] We have discussed the same issues in our original...
Dear Editor
We would like to comment on the paper by Satin et al, [1] which reports an update of a mortality investigation on two cohorts of petroleum refinery workers. The Authors claim that one of the major aims of their study was the assessment of “health risks relative to more contemporary levels of exposure and work environments”. Nevertheless, they explicitly admit that a previous investigation in such...
Dear Editor
The paper by Harrison et al.[1] and the accompanying editorial by Cherrie [2] address the important issue of personal exposure assessment (of air pollutants) in environmental epidemiology. After reading both papers we would like to make some comments with regard to the design, conduct and statistical analysis of the study by Harrison et al. and at the same time answer the question raised by...
Dear Editor
We read with interest the article by Hoogendoorn et al. (2002) who examined the use of different approaches to analysing data from their prospective cohort study of work-related exposures and the future onset of low back pain.[1]
Exposures and outcomes are time dependent factors as they are subject to change over time. The strength of the relationship depends on the assumptions of time depend...
Table 1(i): Work-related mechanical risk factors and new onset low back pain*
...
Table 1(ii): Work-related mechanical risk factors and new onset low back pain*
Univariate associations
...
Table 2: Work-related psychosocial risk factors and new onset low back pain*
...
Dear Editor
The article by Harrison and colleagues’[1] reports on a relationship between personal and static microenvironment air sampling for carbon monoxide and nitrogen dioxide and for PM10 which include the addition "of a personal cloud increment." Static sampling is also commonly referred to as area or stationary sampling.[2,3] These relationships are important because static sampling is more easily achieved th...
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