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Associations between disease and occupation Hypotheses generated from the national mortality followback survey.

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American Journal of Industrial Medicine 27:195-205 (1995)
Associations Between Disease and Occupation:
Hypotheses Generated From the National Mortality
Followback Survey
Rebecca Reviere, PhD, Scott Schneider, MSIH,and Kathy Woolbright, MA
This study uses the National Mortality Followback Survey of 1986 to identify the top
five Sentinel Health Events Occupational [SHE(O)s], the five leading causes of death,
and to ascertain the primary occupations and industries associated with these. We found
that, as expected, cardiovascular diseases were four of the five leading causes of death
overall. In addition, the SHE(0) responsible for most deaths was cancer of the trachea,
bronchus, and lung, followed by renal failure, bladder cancer, myeloid leukemia, and
liver cancer. We employed proportionate mortality ratios to analyze the relationship
between industry and occupation and category of mortality. In brief, we validated
findings by other researchers; for example, farmers were at lower risk of cancer of the
trachea, bronchus, and lung, and workers in eating/ drinking places had excess risk of
liver cancer. We also hypothesize other relationships, such as between motor vehicle
dealers and bladder cancer. o 1995 Wiley-Liss, Inc.
Key words: worker health, mortality, SHEO,cardiovascular disease, cancer, industry, occupation
INTRODUCTION
While estimates of the frequency of illnesses and deaths due to work conditions
vary, their occurrence is sufficient to have attracted the attention of both researchers
and practitioners involved with occupational health and safety. A framework for
identifying such health problems is the Sentinel Health Events Occupational
[SHE(O)]. In 1976, Rutstein et al. developed the original listing of Sentinel Health
Events with the assumption that preventable diseases and untimely deaths could be
used to monitor the quality of available medical care. The success of this approach
with such things as maternal mortality led them in 1983 to apply the method to the
field of occupational health with SHE(0)s. This listing was later up-dated by Mullan
and Murthy [1991]. SHE(0)s are defined as “unnecessary disease, disability, or
untimely death which is occupationally related and whose occurrence may: 1) provide
the impetus for epidemiologic or industrial hygiene studies, or 2) serve as a warning
Department of Sociology/ Anthropology, Howard University, Washington, DC (R.R., K.W.).
Center to Protect Workers’ Rights, Washington, DC (S.S.).
Address reprint requests to Rebecca Reviere, Department of Sociology/ Anthropology, Howard University, Washington, DC 20059.
Accepted for publication March 7, 1994.
0 1995 Wiley-Liss, Inc.
196
Reviere et al.
signal that materials substitution, engineering control, personal protection, or medical
care may be required” [Rutstein et al., 19831.
The purpose of this study was to test whether SHE(0)s are, in fact, validated by
occupational data from mortality statistics and to ascertain whether other SHE(0)s
could be identified from national mortality data bases. Further, we aimed to identify
those occupations and industries whose members are most at risk of the leading
causes of death.
MATERIALS AND METHODS
The data derived from the 1986 National Mortality Followback Survey
(NMFS), funded primarily by the National Center for Health Statistics. The NMFS is
a nationally representative sample (excluding Oregon) of adults ages 25 or over who
died in 1986. The sampling frame was the 1986 Current Mortality Sample, a 10%
sample of state death certificates. A total of 18,733 decedents were weighted to a final
number of 1,986,868 [U.S. Department of Health and Human Services, 19901.
Cause of death was obtained from death certificates and coded according to the
Manual of the International Statistical Classification of Diseases, Injuries, and the
Cause-ofDeath, Ninth Revision (ICD-9). Occupation and industry codes came from
questions regarding ‘‘longest job” and “longest industry” of the decedent. These
were categorized according to the ‘‘Classified Index of Industries and Occupations’’
[U.S. Department of the Census, 19801. The information was supplied by informants
(usually next-of-kin) who completed questionnaires on the decedent.
First, we identified the top five industries and occupations for the five most
common causes of death and for the most common SHE(0)s. We then calculated the
proportionate mortality ratio (PMR) for each relationship. The PMR compares the
observed number of deaths for a particular cause in an occupation or industry group
with the expected number of deaths from that cause, based on the proportion of all
deaths due to that cause in a standard population [Lalich et al., 19901.
RESULTS
First, we examined the top five industries and occupations for the five most
common causes of death. Tables I through IV show this frequency and the distribution
by occupation and industry.
Acute myocardial infarction (Table I) was the most common cause of death.
Manufacturing (not specified) and construction were the industries with excess risk of
this disease. Among individual occupations, we found excess risk among heavy truck
drivers, managers and administrators, and supervisors and proprietors/sales workers.
The second most common cause of death was ischemic heart disease (Table 11).
private households and agriculture were the industries with excess rates of this disorder. Laborers (not in construction) was the occupation with an excess risk. Elementary school teachers also had a slight excess.
Cancer of the lung, trachea, and bronchus was the third most common cause of
death for workers; this is discussed in the SHE(0) category below (see Table V).
The fourth most frequent cause of death in this population was cerebrovascular
disease (Table 111). Excesses were observed in the industries of agriculture and department stores. Occupations particularly at risk were sales workers (not elsewhere
SHE(0)s in Followback Survey
197
TABLE I. Distribution of Acute Myocardial Infarction (ICD-9=410) as Underlying Cause of
Death in the National Mortalitv Followback Survev, 1986*
%
No.
Census code
1060
1010
842
641
392
Census code
473
1019
889
804
243
deaths
Sample
Populationa
PMR
Industry
Construction
Agriculture, crops
Elementary, secondary schools
Eat/drinking places
Manufacturing, not specified
17,829
13,535
9,626
7,849
6,746
6.9
5.2
3.7
3
2.6
6
4.9
4
3.3
2.1
1.15
1.06
0.925
0.91
1.24
Occupation
Farmers, not horticulture
Managers, administrators
Laborers, not construction
Heavy truck drivers
Supervisors, proprietors/sales
12,719
9,974
8,389
6,447
6,032
4.9
3.9
3.2
2.5
2.3
4.4
3.1
3
2
1.9
1.11
1.26
1.07
1.25
1.21
*Total deaths from this cause, 258,795.
"Percentage in this line of work in the total population.
TABLE 11. Distribution of Ischemic Heart Disease (ICD-9=414) as Underlying Cause of Death
in the National Mortality Followback Survey, 1986*
~
Census code
1010
1060
842
641
76 1
Census code
473
889
313
1019
156
%
No.
deaths
Samde
Pouulation"
PMR
Industry
Agriculture, crops
Construction
Elementary, secondary schools
Eat/drinking places
Private households
10,672
8,102
7,719
6,107
6,093
5.6
4.3
4.1
3.2
3.2
4.9
6
4
3.3
2.6
1.14
0.12
1.025
0.97
1.23
Occupation
Farmers, not horticulture
Laborers, not construction
Secretaries
Managers, administrators, nec
Teachers, elementary school
8,241
6,820
5,418
5,191
4,293
4.3
3.6
2.8
2.7
2.3
4.4
3
2.6
3.1
2.1
0.98
1.2
1.08
0.87
1.095
~_____
*Total deaths from this cause, 190,403.
"Percentage in this line of work in the total population.
classified [NEC]), supervisors and proprietors/sales workers, and farmers (not horticultural).
Other heart diseases were the cause of death occurring fifth in frequency (Table
IV). Only minimal excesses were observed by industry. Occupations particularly at
risk were cleaners/servants in private households and farmers (not horticultural).
Next, we examined the top five industries and occupations for the most common
causes of death in the SHE(0) category. Cancer of the trachea, bronchus, and lung
was most common, followed by renal failure, cancer of the bladder, myeloid leukemia, and liver cancer.
Manufacturing (not specified) and construction industries showed excesses of
198
Reviere et al.
TABLE 111. Distribution of Cerebrovascular Disease (ICD-9= 436) as Underlying Cause of
Death in the National Mortality Followback Survey, 1986*
Census code
1010
1060
842
641
591
Census code
473
274
313
889
243
%
No.
deaths
Sample
Population"
PMR
Industry
Agriculture, crops
Construction
Elementary, secondary schools
Eat/drinking places
Department stores
5,087
3,411
3,113
2,728
2,470
6.4
4.3
3.9
3.4
3.1
4.9
6
4
3.3
1.4
1.31
0.72
0.975
1.03
2.21
Occupation
Farmers, not horticulture
Sales workers, nec
Secretaries
Laborers, not construction
Supervisors, proprietorsisales
4,946
3,555
2,169
2,045
1,978
6.2
4.4
2.7
2.6
2.5
4.4
2.5
2.6
3
1.9
1.41
1.76
1.04
0.87
1.32
*Total deaths from this cause, 79,998.
aPercentage in this line of work in the total population.
TABLE IV. Distribution of Other Heart Disease (ICD-9= 429.2) as an Underlying Cause of
Death in the National Mortality Followback Survey, 1986*
Census code
1060
1010
761
842
641
Census code
473
407
889
156
313
%
No.
deaths
Sample
Population'
PMR
Industry
Construction
Agriculture, crops
Private households
Elementary, secondary schools
Eat/drinking places
4,138
3,419
2,636
2,624
2,292
6.1
5.1
3.9
3.9
3.4
6
4.9
2.6
4
3.3
1.02
1.04
1.5
0.975
1.03
Occupation
Farmers, not horticulture
Private household, cleaners/servants
Laborers, not construction
Teachers, elementary school
Secretaries
3,733
2,194
1,954
1,895
1.400
5.5
3.2
2.9
2.8
2.1
4.4
1.8
3
3.1
2.6
1.25
1.78
0.97
0.9
0.81
~
*Total deaths from this cause, 67,499.
aPercentage in this line of work in the total population.
lung cancers (Table V). Occupations particularly at risk were heavy truck drivers,
supervisors, and laborers (not construction).
Excess risk of renal failure (Table VI) was observed among hospitals, eating and
drinking places, and construction workplaces. Cashiers, sales representatives, carpenters, and managers all had excess mortality risk from renal failure.
Excess risk of bladder cancer (Table VII) was found among workers in agriculture, hospitals, and motor vehicle dealerships. Occupations especially at risk were
sales workers for motor vehicles and boats. Machinists, farmers, secretaries, and
managers also had excess rates of this condition.
Myeloid leukemia (Table VIII) showed an excess risk for workers in govern-
SHE(0)s in Followback Survey
199
TABLE V. Distribution of Cancer of the Trachea, Bronchus, andd Lung (ICD-9 = 162.9) as an
Underlying Cause of Death in the National Mortality Followback Survey, 1986*
%
No.
deaths
Sample
Population"
PMR
Census code
1060
392
1010
641
842
Industry
Construction
Manufacturing not specified
Agriculture, crops
Eat/drinking places
Elementary, secondary schools
10,998
4,387
4,145
3,951
3,772
8
3.2
3
2.9
2.7
6
2.1
4.9
3.3
4
1.33
1.52
0.61
0.87
0.67
Census code
889
804
243
1019
47 3
Occupation
Laborers, not construction
Heavy truck drivers
Supervisors, proprietordsales
Managers, administrators
Farmers, not horticulture
5,577
4,884
3,850
3,934
3,551
4.1
3.6
2.8
2.9
2.6
3
2
1.9
3.1
4.4
1.36
1.8
1.47
0.93
0.59
*Total deaths from this cause, 137,374.
"Percentage in this line of work in the total population.
TABLE VI. Distribution of Renal Failure (ICD-9= 584) as Underlying Cause of Death in the
National Mortality Followback Survey, 1986*
Census code
1060
641
831
1010
761
Industry
Construction
Eat/drinking places
Hospitals
Agriculture, crops
Household cleaners/servants
Census code
1019
473
567
276
259
Occupation
Managers, administrators
Farmers, not horticulture
Carpenters
Cashiers
Sales reps
%
No.
deaths
Sample
PoDulations"
PMR
1,400
954
816
689
537
7.5
5.1
4.4
3.7
2.9
6
3.3
2.4
4.9
2.6
1.25
1.54
1.83
0.73
1.11
5
3.1
4.4
1.3
0.6
0.8
1.61
0.84
2.69
926
696
65 1
622
409
3.7
3.5
3.3
2.2
5.5
2.75
*Total deaths from this cause, 18,639.
"Percentage in this line of work in the total population.
ment, department stores, and agriculture. Particularly at risk were sewing machine
operators. Farm workers, sales workers, and managers were also at excess risk.
Excess risk of liver cancer (Table IX) was found in the real estatelinsurance
industry, laundry/dry cleaning industry, eating and drinking places, general government workplaces, and construction workplaces. Miscellaneous machine operators
showed high risk as did carpenters, secretaries, and laborers (not construction).
DISCUSSION
The data are subject to several sources of error. First, cause of death comes from
the death certificate; we have no way of ascertaining diagnostic accuracy. Second, we
200
Reviere et al.
TABLE VII. Distribution of Bladder Cancer (ICD-9= 188) as an Underlying Cause of Death in
the National Mortality Sample, 1986*
%
No.
deaths
Sample
Population”
PMR
Census code
1010
1060
612
842
831
Industry
Agriculture, crops
Construction
Motor vehicle dealers
Elementary, secondary schools
Hospitals
1,484
734
634
519
466
10.3
5.1
4.4
4.2
3.2
4.9
6
0.6
4
2.4
2.1
0.85
7.33
Census code
473
1019
313
263
631
Occupation
Farmers, not horticulture
Managers, administrators
Secretaries
Sales workers, motor vehicle/boats
Machinists
1,278
737
715
427
420
8.8
5.1
4.4
3.1
2.6
0.2
0.9
2
1.65
1.92
15
3.22
5
3
2.9
1.05
1.33
*Total deaths from this cause, 14,445.
“Percentage in this line of work in the total population.
TABLE MIL Distribution of Myeloid Leukemia (ICD-9= 205) as Underlying Cause of Death in
the National Mortalitv Followback Survey, 1986*
%
No.
deaths
Sample
Population”
PMR
Census code
1010
591
1060
901
641
Industry
Agriculture, crops
Department stores
Construction
General government nec
Eat/drinking places
476
412
355
278
269
6.3
5.5
4.7
3.7
3.6
4.9
1.4
6
1.1
3.3
1.29
3.93
0.78
3.36
1.09
Census code
1019
274
477
479
727
Occupation
Managers, administrators
Sales workers nec
Farm supervisors
Farm workers
Sewing machine operators
417
412
217
217
217
5.5
5.5
2.9
2.9
2.9
3.1
2.5
0
1.3
0.2
1.77
2.2
0
2.23
14.5
*Total deaths from this cause, 7,563.
“Percentage in this line of work in the total population.
have only longest occupation and industry, as supplied by informants, to indicate
work history. Again, we do not know the reliability of these reports. We suspect,
however, that using “longest” occupation and industry, even as supplied by informants rather than “usual occupation” as it is coded on the death certificate, should
give more reliable results. We understand that these decedents may have changed jobs
several times in their lifetimes; however, the average length of the longest job category was well over 5 years, long enough for occupational influences to be felt. Also,
because we are examining weighted data, our standard errors are unnaturally low.
Further, our choice of analysis is the PMR. The PMR in itself can be a source
of error because it uses the population of decedents for the denominator instead of the
SHE(0)s in Followback Survey
201
TABLE IX. Distribution of Liver Cancer (ICD-9= 155) as Underlying Cause of Death in the
National Mortality Followback Survey, 1986*
Census code
1060
641
712
77 1
90 1
Census code
777
567
889
869
313
%
No.
deaths
Samule
Pouulation"
PMR
Industry
Construction
Eatldrinking places
Real estatehsurance
Laundrykleaning
General government nec
589
555
404
272
27 1
8.3
7.8
5.7
3.8
3.8
6
3.3
0.9
0.8
1.1
1.38
2.36
6.33
4.75
3.45
Occupation
Miscellaneous machine operators
Carpenters
Laborers, not construction
Construction workers
Secretaries
432
402
333
319
317
6.1
5.7
4.7
4.5
4.5
0.9
1.3
3
1.5
2.6
6.78
4.38
1.57
3
1.73
*Total deaths from this cause, 7,115.
"Percentage in this line of work in the total population.
population-at-risk; this can result in the problems of overstating or understating risk
[DecouflC et al., 19801. In addition, common causes of death may be over and
underrepresented [Milham, 19751. It is, however, considered useful for exploratory,
hypothesis-generation research [Dubrow et al., 19871 which matches our purpose
here, i.e., to investigate possible relationships that may exist for use in further study.
Four of the top five causes of non-SHE(0) death in this study were cardiovascular diseases (CVD) as expected, and we discuss these as a group. A great deal of
research has investigated links between coronary outcomes and occupational factors.
These include specific hazards, such as carbon disulfide and nitroglycerin exposure
[Kristensen, 1989bl and nonspecific job stressors including work overload [House,
19751, shift work, low control [Alfredsson and Theorell, 19831, sedentary occupation
[Kannel and Sorlie, 1979; Kannel et al., 19861, noise [Kristensen, 1989a], and status
[Buring et al., 1987; Dobson et al., 19851.
Groups with elevated PMRs for CVD in this study were managerdadministrators, heavy truck drivers, supervisors/proprietors in sales, cleaners and servants in
private households, sales workers, fanners, and department store workers. These
findings make sense theoretically, fitting within the framework of previous findings.
Cleaners and servants are among the lowest status workers; sales workers could be
expected to have high job strain due to pressure for commissions. Again, job strain
may explain why managers/administrators have relatively high PMRs for CVD.
Heavy truck drivers have a relatively inactive occupation, likely combined with high
rates of cigarette smoking and exposure to carbon monoxide, which is associated with
heart attacks. Manufacturing workers may use chemicals such as methylene chloride
which can also increase risk of heart attack.
The third most common cause of death and most frequent SHE(0) in our
sample, as in other research [Feldman and Gerber, 19901, was cancer of the trachea,
bronchus, and lung. We found, as have others [Dubrow and Wegman, 1984; Ng,
1988; Lynge and Thygsen, 1990a1, that construction and manufacturing industries
had relatively high rates, possibly through asbestos exposure [Glass et al., 19911. In
202
Reviere et al.
addition, associations between driving heavy trucks and this disease have been found
[Dubrow and Wegman, 1982; Ng, 1988; Zahm et al., 19891;exposure to diesel fumes
and motor exhaust [Hayes et al., 19891 may be implicated. Our findings that those
working in farming had relatively low risk supports the work of others [Zahm et al.,
1989; Lynge and Thygsen, 1990a; Frey and Glenn, 19881. It is difficult to interprete
these results without knowledge of cigarette smoking, however.
Renal failure was the second most frequent SHE(0). It has been associated with
exposure to cadmium [Brown et al., 19871, lead [Wedeen, 1983; Lurakis and Pitone,
1984; Van de Vyver et al., 19881, phenol [Foxall et al., 19891, organophosphates
[Bardin et al., 19871, and solvents, such as carbon tetrachloride [Muehrcke et al.,
19761. We observed excesses among workers in hospitals, eating and drinking establishments, and construction sites. Cashiers, carpenters, sales representatives, managers, and administrators also had a high relative risk of renal failure. Construction
workers, including carpenters, can have exposure to solvents which have renal toxicity, as can hospital workers. Lead is also a common exposure among construction
workers doing renovation work on houses or bridges covered with lead paint. Sales
representatives, cashiers, managers, and administrators generally do not have these
types of exposures but could experience job stress which is associated with high blood
pressure, a possible contributor to renal damage.
Excess risk of bladder cancer was found among agricultural workers and farmers, for which there were no proven explanations in the literature. However, La
Vecchia et al. [I9901 reported positive trends for exposure to herbicides, but in
general findings are mixed [Blair et al., 1985; Une et al., 1987; Burkart et al., 1978;
Bross et al. , 19781.
Unexplained risks were also found for secretaries, hospital employees, and
motor vehicle dealers. Motor vehicle and boat sales workers were at high risk for
bladder cancer, results which might be explained by exposure to exhaust and gases
[La Vecchia et al., 1990; Silverman et al., 19861. Other researchers have also found
excesses in machinists and related workers and attribute these to the aromatic amines
commonly used as antioxidants in cutting oils [Milham, 1976; Dubrow and Wegman,
1982; Vineis and Prima, 1983; Vineis and Magnani, 19851. Managers were also
found to have excesses of bladder cancer, a finding supported by other researchers
[Gonzalez et al., 19891.
Myeloid leukemia was the fourth most frequent SHE(0). It has been associated
with several exposures, including low frequency radiation from electrical fields [Juutilainen et al., 19901, benzene [Lamm et al., 1989; Lumley et al., 19901, and ethylene
oxide [Hogstedt et al., 19791. Our data showed higher relative risks of myeloid
leukemia in department store workers, government workers (NEC), agricultural crop
workers, and the occupations of sewing machine operators, farm workers, sales
workers (NEC), and managers and administrators. The industries and occupations at
higher risk generally do not have exposures to known risk factors. The only possible
connection is pesticide exposures of farm workers and potential solvent exposures
among sewing machine operators. Many of these associations are unexplained by
occupation and may be due to small sample sizes.
The fifth SHE(0) in this study was liver cancer. Consistent with other researchers [Lynge and Thygsen, 1990a; Suarez et al., 1989; Ng, 19881, we found that
construction workers had high rates for liver cancer. Other studies have found that,
as in our study, cooks, bar workers, and chefs [Dubrow and Wegman, 1984; Suarez
SHE(0)s in Followback Survey
203
et al., 19891 have increased risk. These workers likely have high rates of alcohol and
cigarette use which may contribute to the disorder [Dubrow and Wegman, 19841. Our
finding that those in the laundry and cleaning industry were at risk replicates other
findings [Nakamura, 1985; Lynge and Thygsen, 1990bl; it has been suggested that in
this environment, tetrachloroethylene exposure increases risk. On the other hand,
McLaughlin et al. [ 19871 did not find an increased incidence of liver cancer among
laundry and dry cleaning workers.
CONCLUSIONS
In conclusion, the occupational origins of many excess risks seen here are
unclear. SHE(0)s are generally discovered because of high rates of a rare disease
among small populations of exposed workers. Our study looked at the most common
causes of death and, consequently, did not prove an effective means of identifying
new SHE(0)s.
Many existing associations were validated, however. The unusually high excesses found (i.e., motor vehicle dealers and bladder cancer) suggest hypotheses
which deserve further exploration, although the magnitude of excess may be attributable to small sample size. In addition, as we investigated mortality, conditions may
be present but not a primary cause of death; they would, therefore, be undetected
here. While the connection with occupation may seem obscure now, perhaps this
method can help find SHE(0)s which have not been readily apparent.
In the long run, hypotheses and statistics generated should be applied to improve work conditions and worker health and safety. To this end, improvements in
data collection would be useful [Feldman and Gerber, 19901. Future studies might
focus on possible links between conditions which are less directly associated with
disease and on intervention approaches which lessen likelihood of illness. At the same
time, the implication of worker health habits, such as smoking and diet, and their
possible additive or multiplicative effect with unhealthy work conditions should be
considered.
ACKNOWLEDGMENTS
This research was funded, in part, by a New Faculty Research Grant (19205)
from Howard University.
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