r -; Review Article

••

A Comparison of PMRs and SMRs as Estimators of Occupational Mortality

Robert M. Park, 1 Neil A Maizlish / Laura Punnett ,3 Rafael Moure-Eraso/ and Michael A. Silverstein"

Standardized mortal ity ratios (SMRs) fo r occupational diseases are confounded br health dttferences between industrial and gene ral populations. In I 09 indus m al cohorts largely free of work-related·mortality, these selection etfects were sizable for both malignant and nonmalignant outcomes. All -cancer SMRs were considerably less than 1.0 for many cohorts , and lung cance r was subject to

almost as much selection-de rived confounding as nonmalignant disease. Standardi:ed proport ional mortal ity ratios (P\I!Rs) (approximated by relative SMRs (RSMRs)) were less confounded than SMRs in estimating occupational risk. PM Rs appeared to overestimate cancer mortality on average by 6%, while SMRs underestimated by 13%. PMRs underestimated nonmalignant respiratory disease by 16 percent but SMRs underestimated by 39 percent. The sources of confounding, in addition to selection on health scatus at hire , mos t likely include social class. SMRs, in the absence of internal population comparisons , would fail to detect bo th malignant and nonm:!lignanr work- relared rnorr ::!liry in rne.ny industrial cohorts . (Epidemiology 199 1 ;2:49-59)

Keywords: mortality, occupational diseases, PMR, selection confounding, S!vfR, social class.

Standardized mortality ratios (SMRs) are the preferred basis for inference in occupational mortality studies when measures of exposure are unavailable for internal compar­isons (1 ,2) . In contrast, standardized proportional mortal­ity ratios (PMRs) are widely regarded as deficient (1,3--8). One study comparing PMRs to SMRs in a cohort of construction workers found several causes of death with statistically significantly increased risk, using PMRs, where there was none according to SMR analysis (9). The authors, assuming SMRs to represent true risk, resolved that no conclusions should be drawn from PMR analyses. Most theoretical discussions have focused on evaluating various alternative mortality measures as approximators of the SMR, the mortality "gold standard" (4--8) . Many published mortality studies reflect this view; the editorial practices of important sciemific journals resist PMR and favor SMR studies.

Nevertheless, noncomparability between occupational cohorts and reference populations is widely recognized in the "healthy worker effect" (10-13). At its simplest, ·industrial workers have less-than-expected mortality for cardiovascular and respiratory disease because employ· ment selects against impaired health status. This effect

From the 'Health and Safetv Department. United Auto Workers International Umon. Detroit: 'Occupational Health Program. State oi California. &rkdev: 'Department ,,i Work Environment. Univermy oi Lowell. MA: and the ' Department oi Health. State oi Washington. Address repnnr requ~sts to Rol-:..,rr Park. Health and Safety Depart· ment. United Auto Workers lnrcrnanonJI l!ninn. 8000 E. jefferson Avenue, Detroit . ~\I -+8214.

C 1991 Epidemioloi(Y Resources Inc.

has been described in relation to age at hire, current employment status, and durations of employment and follow-up ( 11) . Some have argued that the selection is more complex, depending, for example, on superior medical care or prospects for long-term · advancement (T2). Employment-related selection may be only one consideration, however, because other factors such as social class also distinguish industrial cohorts from the general population. Carpenter has enumerated several potentially important features of social class confound­ing, suggesting that malignancies are affected as much as other diseases ( 13).

To assess the noncomparability of study and reference populations, we compiled from the epidemiologic litera­ture a large number of industrial cohorts relatively free of work-related mortality. We examined the systematic bias of SMRs and PMRs (approximated by relative SMRs (RSMRs)) away from the null value, 1.0.

Methods One hundred and nine industrial cohorts were selected from 90 mortality studies published in the British Journal of Industrial Medicine, the American Journal of Industrial Medicine, the American]ournal of Epidemiology, the]ournal of Occupational Medicine , and the Scandinavian Journal of Work En\·ironment and Health , for the years 1981 through April or \i.ay 198 7 {14-1 0 3) . Cohorts were selected provided there were 200 or more deaths, numbers of deaths and SMRs (or expecred deaths) were preswted at least tor ai! causes and all cancers, a national reference popu;ation was used, and strong evidence of substant ial

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PARK ET AL

TABLE 1. Bias in Estimators of Occupational Mortality: Mean ln(SMR)s and ln(RSMR)s for Sets of Cohorts Selected for Absence of Work-Related Mortality

Mean ln(SMR) Mean ln(RS!v!R)

All Lung All All Lung All Cohort Set Cancer Cancer Noncancer 0JMRD* Cancer Cancer Noncancer i':~!RD

Largely tree oi work-related mor-talicy (n = 109) - .08 1 - .041 - 181

Excluding suspect cohomt (n = 951 - .108 - .077 - 207

"Unexposed" cohortS~ (n = 79) -.122 - .108 - .206 Adjusted ior attribu table deaths§

(n = 79) -.140 - .147 - .208

*NMRD = nonmalignant respi ratory disease. t 14 cohorts excluded because of probable work-related mortality. :j:Remaming cohorts from asbes tos, smelter and chemical industries excluded.

r, ___ ,_ 075 .112 - 024 - II~

- .336 076 .104 - 024 -.146 -.332 064 .075 -.020 - 145

-.353 .052 .043 -016 - 161

§Cause-speci ric attributable deaths were deleted where evidence of work-relatedness was a\'atlable.

occupation-attributable mortality was absent. Cohorts rejected owing to attributable mortality numbered 16, including workers at a chromate smelter ( 104), tin miners (105), nine asbestos-exposed cohorts (106-114), chro· mate pigment producers (115), coal miners (116), other miners selected by compensation claims ( 117), cryolite refiners ( 118) and chimney sweeps ( 119). In addition, in five studies, some subcohorts were rejected because of work-related mortality (56,64, 70, 75,89).

Within the 109 cohorts that we judged to be largely free of work-related mortality, we suspected 14 of having important work-related mortality (14-26) despite evi­dence that was weak. These cohorts were excluded from most analyses, leaving a more nearly pure set of 95 cohorts. Excluded were five cohorts with likely high asbestos exposures (16,19,20,23,24); a copper smelter (14), a steel foundry (17) , two lead-exposed cohorts (21) ; a nickel-cadmium battery cohort (1 5), ceramic nuclear fuels workers (26), a fragrance and flavor manufacturer (18), chrome platers (25), and a nitrate fertilizer manufac· turer (22) .

Cohorts from three industries, classified generically as having work-related mortality, were further excluded: the remaining asbestos (35), smelter (34,60,84), and chemi­cal manufacturer (29,36,51,59,61,75,83,86,88,91,92,100) cohorts. The smelter cohorts showed sizable and statisti· cally significant lung cancer and nonmalignant respira­tory disease excesses. Some chemical manufacturer co· horrs with likely toxic exposures showed statistically significant excesses for malignant or nonmalignant causes of death (51,61 ,83,92), while most exhibited no apparent attributable mortality. All were excluded generically to avoid introducing bias. Excluding these three industries left 79 "unexposed" cohorts (Table 1).

In a number of the "unexposed" cohorts, there ri~-

50

mained evidence of cause-specific mortality attributable to a particular exposure but affecting small proportions of deaths. For some analyses, adjustments were made to

SMRs by deleting obse rved deaths to make the cause­specific SMR equal 1.0. Three criteria defined amibut­able deaths: (a) well-established associations, for exam­ple, lung cancer with coke-oven exposures (42); (b) similar patterns in more than one cohort, for example, lung cancer in dockyard workers in two countries not explained by smoking differences (62,90), lung and liver cancer and multiple myeloma in rubber workers (27 .38,50,81); or (c) strong evidence of dose-response or duration and latency trends within the cohort ( 49,81). Overall, we made adjustments conservatively, affecting 2 7 of the 79 "unexposed" cohorts (34%) but involving only 0. 7% of total deaths, and 2.6% of cancer deaths.

\Ve computed RSMRs, the cause-specific SMR divided by the all-causes SMR, as a surrogate for PMR, which was unavailable in almost all studies. (None of the studies presented standardized mortality odds ratios along with SMRs.) As predicted by Kupper et al on theoretical grounds (120), the RSMR has been found to be a close approximation to the PMR (9,120,121). usually within 1 or 2%. (In a cohort of construction workers (3,345 deaths), the RSMRs and PMRs, respectively, for all cancer, lung cancer, digestive cancer, circulatory disease, and nonmalignant re~ viratory disease were: 1.158,1.1 SO; 1.323,1.308; 1.014,1.015; 0.917,0.922; and 1.162,1.143 (9).)

The all-nonmalignant disease SMR was chosen to be a measure of cohort selection relative to the nationa l porulation. Unweighted least square regressions were computed to describe trends of various other SY!Rs anJ R.S\1Rs on the all-nonmalignant disease SMR. (SMR> anj RSMRs were transfom1ed to their natural lur;arithm

Epider.'liology January 1991. Volume 2 '(umber I

• to stabilize variance owing to widely varying cohort sizes.) Because the nonmalignant disease SMR was a random variable, the va riance of the regression coefficients was es timated using a gene ral linear model in which the independent variable is a random variable and the error variance is not assumed uniform (122). (See Appendix.)

The logarithm of a cause-specific SMR, expected to take the value 0 .0 in the abse nce of selec tion-confound­ing (bu t usually < 0.0), was used as a measure of SMR bias. The logarithm of the RSMR, also expected to be 0.0 (bu t often greater), was the measure of RSMR bias. The percent estimation error fo r SMRs and RSMRs was defined as the absolu te value of the deviation of the estimator geometric mean from 1.0, divided by the estimator geometric mean. (Geometric-mean SMR = exp(mean ln (SMR)).)

Results All but 8 of the 109 cohorts had a minimum employment duration of less chan 5 years, and the follow-up period usually exceeded 20 years . Latency was not addressed in most of the SMR studies in chis survey. Eighty-six of the cohorts consisted of white, male workers, and 80 of these were largely or entirely hourly employees. Selection bias was not an issue because deaths not known to the employer had been systematically searched for in all but a few instances (3 7,6 7 ,68). The major industrial groups represented were oil refining (n = 15), chemical (n = 13), rubber (n = 13), and nuclear fuel (n = 6) cohorts.

For the 109 selected cohortS (including 14 with po tentially important work-related mortality), the mean log all-cancer RSMR (RSMR bias) was 0.075, and the mean log all-cancer SMR (SMR bias) was -0.081 (Table 1) . When the 14 cohortS with suspect work-related mortality were removed, the all-cancer RSMR bias was 0.076 and the SMR bias was -0.108 (Table 1). After the asbestos, smelter, and chemical cohorts were generically excluded, the all-cancer RSMR and SMR biases in the 79 remaining cohorts were 0.064 and -0.122, respectively (Table 1) . Thus RSMRs (and presumably PMRs) ap­peared to overestimate cancer mortality on average by about 6% ((exp( .064) - 1)/exp(.064) = 0.062), while SMRs underestimated by 13% ((1- exp(- .122))/ exp(- .122) = 0.130). When small numbers of individual work-attributable deaths were deleted from the 79 co­horts, RSMRs overestimated cancer risk by 5.1% and SMRs underestimated by 14.8%.

For all-nonmalignant causes of death, the RSMR bias was -0.02, while the SMR bias was -0.21 (Table 1) , corresponding to estimation errors of 2.0 and 23%, respectively.

The plot of all-cancer SMRs versus all-nonmalignant

Epidemiology January 1991, Volume 2 Number 1

PMRS AND SMRS I~ OCCUPATIONAL MORTALITY

2.4

2.2

2.0

L>J 1.8

~ (/) u 0

~ 1.4

(!)

~ 1.2

iO 1.0 0:: :::1! (/) o.a

0.11

Q.4

D.2 D.2 0.4

I I II

1 '!\_.i I I

II ~ ... "111 .!.'If I I

tl f 'jtl I I I I

I I I I

0.11 o.a

SMRa: NON-t.W..JGNANT DISEASE

1.4

FIGURE 1. SMRs for all malignant causes of death vs all­nonmalignant causes for i9 "unexposed" cohorts.

causes S}.·!Rs (both untransformed) for the 79 cohorts showed a clear positive association (Fig. 1) . Using log-transformed SMRs, the least squares regression coeffi­cient was 0.70 (9 5% confidence interval (Cl): 0.48-0.91), indicating that the all-cancer SMRs varied almost as rapidly as nonmalignant ones under the selection forces for employment (Table 2). (Because ln(l + x) = x for x « I. it follows that in the vicinity of SMR = 1.0 or ln(SMR) = 0.0: (a) SMR - 1 = ln(SMR) and (b) the rates of change of the log transformed SMRs approximate the rates of change of the SMRs.) The plot for the full set of 109 cohorts was very similar with a coefficient of 0.64 (data not shown). When work-attributable excess death:; were deleted from the 79 cohorts, the regression coeffi­cient was 0.63 (Cl: 0.42-0.85) . Regression of the all-

. cancer RS~1R with nonmalignant cause SMRs (in 79 cohorts with no deletions) produced a smaller, negatiYe slope (-0.23) (Table 2) .

Lung cancer is often of prime concern in occupational cohorts. For the selected 79 cohorts (excluding 5 in which lung ..:ancer was not reported), the RSMR bias for lung cancer was 0.075, and the SMR bias was -0.108 (deleting attributable deaths, the respective values were 0.043 and -0.147) (Tahle 1) . The plot of lung cancer SMRs against all-nonmalignant SMRs (untransformed) showed greater depende:Ke of lung cancer on selection

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• PARK ET AL

TABLE 2. Least Square Regressions of Observed ln(SMR)s and ln(RSMR)s for All Cancer, Lung Cancer, and Nonmalignant Respiratory Disease on Observed All-Nonmalignant Causes ln(SMR)s for 79 "Unexposed" Cohorts

Dependent Variable A* exp(A)

All cancer ln(SMRl 0.022 1.022 ln(SMR)t -0.008 0.992

ln(RSMRl 0.01 i 1.017 ln(RSMR>t - 0.006 0.994

Lung cancer ln(SMR) 0.153 1.165 ln(SMR)t 0.070 1.073

ln(RSlvlR) 0.1 41 LiS i ln(RSMR)t 0.066 1.068

Nonmalignant respiratory disease ln (SMRl -0.025 0.975 ln(SMR) t -0075 0.928

ln(RS~IRl ln(RS:'viR)t

-0.022 - 0.066

0.978 0.936

B

0.698 0.632

- 0.230 -0.280

1.268 1.044

0.320 0.114

1.488 1.338

0.595 0.453

95% CL (B)

0.48. 0.91 0.42, 0.85

- 0.39,-0.07 - 0.44,- 0.12

0.83, 1.71 0.68, 1.41

- 0.09, 0.73 -0.22, 0.45

1.01, 1.97 0.93, 1.74

0.12, 1.07 0.05, 0.86

·~lodels : ln(SMR(i)) = A + B •in(SMR(nonmalignant)) . and ln(RSMR(i)) =A+ B •in(SMR(nonmalignam) ), where A. intercept, is predicted ,·alue of dependent variable at ln(SMR(nonmalignant}) = 0.0, and B is regression coefficient. (Exp(A) is predicted SMR or RSMR when SMR(nonmalignant) = 1.0.) tSMRs and RSMRs computed after deleting deaths attributable to workplace exposures. (See section on methods.)

than that observed for nonmalignant causes of death (Fig. 2); with log-transformed SMRs, the regression coefficient was 1.27 (Cl: 0.83-1.71) (Table 2). Several outlying points suggested residual work-related excesses. Deleting attributable deaths, the coefficient was 1.04 (CI: 0.68-1.41). For all malignancies excluding lung cancer, the RSMR bias was 0.054 and the SMR bias was -0.130, both close to the all-cancer observations (0.064 and -0.119, respectively, in 74 of the 79 cohorts with lung cancer data).

Both RSMRs and SMRs underestimated nonmalignant respiratory disease. In the "unexposed" cohorts (exclud­ing 11 in which nonmalignant respiratory disease was not reported), the RSMR bias for nonmalignant respiratory disease was -0.145 and the SMR bias was -0.332 (deleting attributable deaths, the corresponding values were -0.161 and -0.353) (Table 1). The underestima­tion by RSMRs was 15.6% and by SMRs, 39.4%. The plot of nonmalignant respiratory SMRs against all-nonmalig­nant disease SMRs showed a stronger association than that of lun~ cancer (Fig. 3) . With Joe-transformation. the regression coefficient was 1.49 (Cl: l.Ol-1.91) (Table 2).

52

0:: UJ (.) z 6 <.:) z :::> ...J

a; 0:: :I (I)

2.4

2.2

2.0

1.8

1.8

1.4

1.2

1.0

o.a

o.a

0.4

D.2 D.2 0.4

' I I I I I I I I I I

I I

I ~ I .. I

I I Ia I I 8 ..

I I I II~ I I

I I' l" I I I Ill I

I

o.a o.a 1.2 1.4 I

u

St.IRa: NON-W.UGNANT' DISEASE

fiGURE 2. Lung cancer SMRs vs all-nonmalignant cause SMRs for 79 "unexposed" cohorts.

Deleting attributable deaths reduced the coefficient to 1.34.

In the set of 79 "unexposed" cohorts, the 20 with the lowest nonmalignant cause SMRs were compared with the 20 cohorts having the highest nonmalignant SMRs on mean duration of follow-up. The mean maximum follow-up among cohort survivors (time from cohort opening date to follow-up closing date) for the two groups of cohorts was the same, 30 years, but the mean minimum follow-up among survivors (from cohort clos­ing date to follow-up closing date) was 4.6 years for the low SMR cohort group and 9.4 for the high SMR cohorts . The majority of cohorts in either set, however, had a minimum follow-up of 2 years or less (the median minimum follow-up was 0 and 1 year in the low and high SMR cohorts, respectively) .

SMR.s differed by industrial type (Table 3) . Oil refinery and :1uclear fuels cohorts were apparently among the groups most highly selected for low mortality risk. The chemical and petrochemical worxer cohorts had similarly low nonmalignant SMRs, but their all-cancer SMRs were shiited to higher values.

MCRTALIH ASD SOCIAL CLASS

Sc-cial class is a determinant of mortality through educa­tion, life-style, diet, living environment, and health ca re,

Epidemiology January 1991. V,JJume 2 :\umh:r I

• PMRS AND SMRS I:\ OCCU PATIO NAL ~10RTALITY

2.4

2.2

~ 2.0 (/)

0

~ 1.8

~ 1.11

I than fiveiold . These rates imply that social class is an important potential confounder in occupational mortal­ity studies ior most cancer sites . (In the United Kingdom, cancers oi the brain. eye, and testis, multiple myeloma, and some leukemias increased with social class status, while cancers of the intestine, tongue, hypopharynx, pancreas, and breast. sarcomas , and Hodgkin's disease, among others, were largely independent of social class e;

w 1.4 a: ;, (123) .)

~ 1.2 <-'

~ 1.0 I z

0 o.a z iD a: 0.6 :::::E (/)

0.4

0.2

0.2

FIGURE 3.

I • • I I

\ 1, 11111

11 ., 11 I I I I

1 1.:1 1 .. I ., .. I

• IIIII

0.4 0.6 o.a

SWU: NON-I.W.IG~ DISEASE

S.\{Rs for nonmalignant respiratory disease vs all-nonmalignant causes for 79 "unexposed" cohorts.

in addition to occupational exposures. In Britain, where social class is encoded in mortality records, the SMR.s for all causes and for all cancers nearly double from the highest social class stratum (professionals) to the lowest (unskilled laborers) (123) (Table 4) . Standardized mortal­ity ratios for both stomach and lung cancer increase about threefold from the upper class to the lowest (124). For nonmalignant respiratory diseases, the increase is more

Social class confounding is manifest in mortality studies of professional groups (40,4 7 ,59,65, 125-129) (Table 5) . The SMRs for both malignancies and all causes were typically in the range 0.5 to 0. 7. In a cohort of construction equipment operators (65), the higher-status engineers had all-cause and all-cancer SMR.s of 0.61 and 0.64 (Table 5) ; mechanics (396 deaths) had all-cause and all-cancer SMR.s of 0.64 and 0.80 (65). In the entire cohort (3 .34 5 deaths), the corresponding SMR.s were 0.81 and 0.93. (Exposure to diesel exhaust was a poten­tial confounder in this comparison.) The social class dependence of lung cancer has been investigated by Devesa and Diamond using Third National Cancer Survey data (130). In males, age-adjusted lung cancer incidence rates declined by 50% from the lowest of five income strata to the highest.

Discussion This sur.·ey of industrial cohorts calls into question the practice of computing SMR.s without doing internal exposure comparisons. The results were essentially the same whether or nor small numbers of cohorts or individual deaths were excluded because of potential work attributability. The selection affected both nonma­lignant and malignant causes, demonstrating that the

TABLE3. Standardi:.ed Mortality Measures for Selec ted Industries among 95 Cohorts: Largely Free of Work-Related Mortality and Excluding 14 Suspect Cohorts

Mean SMR* Mean RSMR*

All All Lung All All Lung All Industry nt Causes Cancer Cancer Noncancer NMRDt Cancer Cancer Noncancer NMRD

Chemical 12 .785 .932 .970 .742 .639 1.188 1.235 .944 .812 Mining ; .919 . .975 1.089 .908 .868 !.C61 1.184 .987 .946 Nuclear fuels 6 .786 .824 .916 .776 1.03i 1.048 . 1.166 .986 1.245 Oil refining 15 .757 .810 .723 .745 .55 3 I.C'69 .954 .983 .731 Petrochemical 3 .860 .961 1.055 .825 .743 1.117 1.228 .960 .864 Rubber 13 .824 .900 .859 .805 .7C'S I.C91 1.045 .976 .881 Shoe 3 .789 .727 .465 .803 .921 .589 1.017 Smelter 3 1.050 1.101 1.679 1.038 1.165 l.C'48 1.598 .988 1.158 All 95 .8 32 .898 .926 .813 715 l.C'79 1.110 .976 .864

*Geometric mean= exp (mean !In (SMR))). or =exp (mean (In (RSMR))) . tNumber ot cohom. ;NMRD = nonmalignant respiratory disease.

Epjdemiology January 1991. Volume 2 Number i 53

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PARK ET AL

TABLE 4. Standardized Mortality Ratios for Men in Social Class Strata in England and Wales*

All All Lung Stomach Cdun Social Class Causes Cance r Cancer Cancer Cancer N~IRDt

Professional 0.77 0.75 0.53 l'.50 105 0 37 Managers, employers 0.81 0.80 0.68 0.66 100 0.53 Skilled, nonmanual 0.99 0.91 0.84 .:'79 105 oso Skilled manual 106 1!3 118 I IS 106 106 Semtskilled 1.14 116 123 1~5 101 123 Unskt!led IJ7 1.31 1.43 Ui 1.:'9 lSi

*From Regtmar General 's Report ( 123); all Jeaths ior ages 15-64 tn the l"ear> 1 '-li 2-197 2. tNMRD =nonmalignant resp1racory disease.

source was more general than simply physical fitness at hire. Other surveys have found a similar distribution of all-cause SMRs but apparently did not examine corre­sponding all-cancer and other cause-specific SMRs or attempt to exclude cohorts with work-related mortality (121,131). The survey by Meijers et al reported associa­tions between the healthy worker effect (defined as an all-cause SMR < 1.0) and a variety of cohort design characteristics ( 131). Many of these associations, how­ever, may have been confounded both by exposures and the more general population selection that we have described. For example, the association of healthy worker effect with cohort size is readily explainable by this selection (see below). Whatever are the determinants of diminished mortality, the results of our survey demon­strate that proportional mortality measures are less affected by them than are SMRs.

SMR bias owing to selection confounding would not be a problem where high risks are observed. Typically, however. SMRs are calculated on aggregate populations, often before conducting an exposure assessment. SMR

analyses stratified on exposure categories, and tests for trends, are commonly performed only after statistically significant O\·erall excesses have been observed. Underes­timation by S:\lRs could preempt such analyses.

Several authors have proposed that PMR or RS:\!R analyses may partially correct for noncomparabiliry be­tween cohorts and reference populations (120, 121, 132). Mortality odds ratios (MORs) are a further enhancement of this approach ( 133). The analysis here supports the use of RSMRs anJ. therefore , PMRs and :\lORs (6,8,133) (provided the !='Opulation at risk has been appropriately specified and viral status determined) . For causes of death that show atypical dependencies on the selection factors operating, selection confounding could be worse for either SMR or PMR analyses.

PCMR or proportional cancer mortality ratio analysis has also been used to address selec tion confounding (1,6.9). Besides 1:-eing limited to malignancies, this analy­sis can substantially underestimate risk. For example, the occurrence of a 2.0-fold increase in lung cancer risk would increase the expected number of all cancers (based

TABLE 5. Standardized Mortality Ratios in Professional Class Strata from Published Studies Using National Reference Populations

SMR

All All Lung Population n* Cause Cancer Cancer ~oncancert

Architects( 126) 80 0.7 0.6 1.1 0.7 Chemists(l27) 95 0.55 0.66 0.28 0.52 Chemists( 128) 198 0.47 0.50 0.29 0.47 Construction engineers(65) 364 0.61 0.64 0.60 0.60 Electncal engineers( 126) 108 0.5 0.5 OA 0.5 Pathologists ( 12 5) 110 0.56 0.61 0.41 0.55 Plant managers (59) 66 0.65 0.52 0.0 0.68 Refinery managers (129) 142 0.58 0.65 0.20 0.56 Refinery managers (40) 300 0.6i 0.79 0.81 0.64 Sabricd petrochemical workers(4 7) i2 0.58 (' .i 3 0.44 0.55

*N umb.,r of deaths 10 cohort. tAll n• •nmalignar.t causes oi death.

Epidemiology January 1991, Volume 2 \:umber I

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on proportions) by a factor of about 1.4 and reduce the lung cancer PCMR to l/1.4 = . 71 of the true relative risk (on average) or !.43 in this example (assuming 40% of all cancer deaths are expected to be lung cancer).

The obse rvation that lung cancer varies as rapidly as nonmalignant causes across cohorts is striking. Smoking behavior probably varies strongly along the gradients of other selec tion factors such as those subsumed under social class. This finding implies that smoking behavior in many large industrial cohorts is less than that of the general population and that SMRs for lung cancer could be seriously underestimating attributable risk in cohorts that have relatively low all-cause SMRs. For example, a cohort with an SMR of 0. 7 5 for all nonmalignant causes (corresponding to an all-cause SMR of perhaps 0.8) has a predicted lung cancer SMR of exp(O.l53 + 1.268 ln(0.75)) = 0.8!. Several cohorts in this survey had low to moderate all-cause SMRs (ie, < 0.85) but lung cancer SMRs that exceeded l.Z (56,71,74,92), indicating that important work-relar"d lung cancer may have been presenr .

Mortality analysis is not a sensitive means for detecting nonmalignant respiratory disease. Nevertheless, it may be the only practical avenue in most surveillance situations. Because of selection confounding, it is highly inappropri­ate to rely on SMR measures for this disease category. In this survey, the SMR underestimation of nonmalignant respiratory disease mortaliry by 39% would be sufficient to obscure high levels of risk in exposed subpopulations.

Confounding by socioeconomic status within the work­ing class may be important in occupational epidemiology because many study populations are drawn from relatively large private employers that may be more selective in hiring and provide higher wages and benefits than employers generally. In this survey, 51 of the 109 cohorts (4 7%) were from plants whose population exceeded l ,000; undoubtedly some of the remaining cohorts came from firms with more than l ,000 total employment. In the United States, in 1985, only 13.1% (n = 10.6 million) of nongovernmental employees worked for firms with 1,000

· or more employees (134). Thus the mortality experience of the popu lations studied, in the absence of work-related mortality, could be considerably better than the national average .

Length of follow-up affects cohort mortality because curr.~ ntly or recently employed workers would be ex­pected ro be healthier than those removed from employ­ment for some rime (11, 13). The difference in average minimum follow-up observed here, comparing low and high mortality cohorts, is difficult to interpret. Cohorts with larger minimum follow-up would exclude more of those most recently hired. The ol:-served difference could

Epidemiology January 1991, Volume 2 Number I

PMRS AND St--IRS lN OCCUPATIONAL MORTALITY

perhaps account tor some of the variance in mortality across cohorts, but it is difficult to explain an effect on malignant causes of death.

Selection at hire appears to take two forms: a transient one diminishmg over time , and a fixed one presumably derived from largely constant individual characteristics such as diet or smoking behavior (11). The relatively fixed characteristics selected by way of respiratory and cardiovascular status (eg, smoking, diet) may also predis­pose to lower risk for malignancies. But fixed selection would also reflect othe r factors related to social class. The precise sele.:tion acting on a cohort will not generally be apparent. Social class attributes are not readily quanufi­able in occupational cohorts anv more than are smoking, dietary, or ethnic risk factors. It is often assumed that local reference populations are preferable to a national one. If social class is an important confounder, however, local rates would help only if the social class characteris­tics of the study population were more similar to those of the region than to the national population. In some situations. the opposite would be true.

SelectiL1n confounding appears to be particularly strong in the oil refine!'\·, petrochemical, chemical, and nuclear fuels cohorts in this survey. Many of the negative findings in these and other previously published studies may need to be reevaluated (29 ,40,44,49,65,66,67, 74,77,91, 92,96,98.99) .

It is now possible, using a national reference popula­tion, to adjust for selection confounding while conduct­ing inremal exposure comparisons. For proportional mortalitY study designs, logistic regression can generate extemallv standardi:ed mortality odds ratio estimates of selection and exposure effects (135-137). (For cohort or follow-up study designs, Poisson regression gives analo­gous results (138, 139)) . Whether in the surveillance context or in analytical studies, the cost advantage of proportional mortality over cohort designs, largely from the restriction of most data gathering to the decreased subset, makes the PMR or better, the mortality odds ratio, particularly attractive measures. The results here justify using such measures.

Acknowledgments

The author~ thank James M. Robins for guidance on the regression analysis; ~lauri:io Macaluso and El1:abeth Del:cll for their suggestion on measurem.:nr or bias; Franklin E. ~lircr tor analytical suggestions and comments: Jnd Pamdd Poe. Kim Brdlos. Su:ann.: Baroli, and Michelle Favad fpr c1encal as,15 tance.

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39. ~leinhardt 1], Lemen RA, Crandall ~IS. Young RJ. Environmen­tal epidemiologic investigation of the styrene -butadiene rubber industry. ScanJ J Work En vir Health !982:8:250-259.

40. Wen CP, Tsai SP, McClellan W A. Gibson RL. Long-term mortality study of oil refinery workers. !. Mortality of hourly and salaried workers. Am J Epidem1ol 198 3: 118:526-542.

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52. ~lcDowell ME. A morwlitv stud;- of c~ment workers. Br J lnd Med ; 9">4:41: 179-182.

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53. Schenker MB. Sm1th T, Munoz A. Woskie S, Spe1:er FE. Diesel exposure and mortality among railway workers: results of a eliot study. Br J lnd ~1ed 1984:41 :320-327.

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66. Checkoway H. Mathew R.\1, Shy CM, Warson JE, Tankersley WG. Wolf SH. Smrth JC. Fry SA. Radiation, work experience, and cause speC! tic mortality among workers at an energy research laboratory. Br J 1nd Med 1985:42:525-533.

67. McCraw OS, Joyner RE. Cole P. Excess leukemia in a refinery population. J Occup \1ed 1985:27:220--222.

68. Hanis NM. Shallenberger LG, Donaleski DL, Sales AE. A retrospecnve mortality study of workers in three major U.S. refineries and chemical plants. J Occup Med 1985;27:283-292.

69. Lawler AB, Mandel JS, Schuman L\1, Lubin JH. A retrospective cohort mortality study of iron ore (hematite) miners in Minnesota. JOccupMed 1985:27:507-517.

70. Gibbs GW. Mortaliry of aluminum reduction plant workers, 1950 through 1977. J Occup Med 1985:27:761-770.

71. Checkoway H. ~1arhew R.\1, Hickey JLS, Shy CM, Hams RL, Hunt EW, Waldman GT. Morrahry among workers in the Florida phosphate industry. J Occup Med 1985;27:885--892.

72 . Becker N, Claude J, Frenr:d-Beyme R. Cancer risk of arc welders exposed to fumes containing chromium and nickel. Scand J Work Envir Health 1985:ll:i5--S2.

73. Pippard EC. Ach~son ED. The morraliry of boor and shot! makers, with special rererence to cancer. Scand J Work Em·ir Health 1985:11 :249-255

74. SW<!<!n<!y MH. Walrath J, Waxweiler RJ. Mortality among re tired fur workers: Dyers, dressers (tanners) and service workers. Scand J Work Envir Health 1985:11:257-264.

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75. Hodgson )T. )ones RD. Mortahty of sryrene producuon, pol;mer­izatinn .1nd process111g workers at a SJte in northwest England. Scandj \X'ork Enm Health 1985:11:347-352.

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78. Vena )E. Vicllann )\1. Marshall J, Fiedler RC \1ortalirv of a munic:tpal wt>rker cohort: Ill. Police Otficers Am J lnd \1ed 1986: ll' 3S3-39i .

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80. Harrington )M. Goldblatt P. Census based morraliry study ot pharmaceuncal industry workers. Br J lnd Med 1986:43:206-211.

81. Sorahan T, Parkes HG. Veys CA. Waterhouse )AH. Cancer mortalitv in the British rubber industry: 1946--80. Br J Ind Med 1986:43:363-373.

82. Johnson ES. Ftschman HR. Maranoski GM. Db:nc~.d E. Occ:.:r renee of cancer in women In the meat mdustry. Br J lnd ~1ed 1986:43 59i-6C4.

83. Bond GG. McLaren EA. Baldwin CL, Cook RR. An update of morrahtv among chemtcal workers exposed to ben:ene. Br J Ind Med 1986:43:685-691

84. Gerhardsson L, Lundstrom N-G, Nordberg G, WallS. Mortality and lead exposure: a retrospective cohort study of Swedish smelter workers . Br J lnd \1ed 1986:43'707-712.

85. Johnson ES. Ftschman HR. Maranoski GM, Diamond E. Cancer mortality among whtte males in the meat mdustry. J Occup \1ed 1986:23:23-32.

86. Pifer JW, Hearne FT, Friedlander BR, McDonough JR. Mortality study of men employed at a large chemical plant, 1972 through 1982. J Occup Med 1986;28:438--444.

87. Kaplan SO. Update of a mortality study of workers in petroleum retinenes. J Occup \1d 1986:28:514-516.

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90. Gusrafsson L ~·all S, Larsson L-G. Skog B. Mortality and cancer incidence among Swedish dock workers-a retrospective cohort studv. Scand J Work Enm Health 1986: 12:22-26.

91. Coggon D, Pannett B. Winter PO, Acheson ED, Bonsall J. Morta!m· of workers exposed to 2 methyl-4 chlorophenoxyacetic acid. Scand J Work Envir Health 1986:12:448--454.

92. Hours ~I. Bertholon J, Esteve J, Cardis E, Freyssinet CL, Quelin P, Fabry J. Mortality experience in a polyamide-polyester factory. Scand J Work Em-ir Health 1986:12:455-460.

93. Gustavsson P, HogsreJr C, Holmberg B. Mortaliry and incidence of cancer among Swedish rubber workers, 1952-1981. Scand J Work En vir Health 1986; 12:538-544.

94. Robinson CF. Waxwerller RJ, Fowler DP. Morraliry among producnon workers in pulp and paper mrlls. Scand J Work En vir Heallh 1986:12:552-560.

95. Thomas TL Stev•art P A. Mortality from lung cancer and respira­tory dL<e:!se among pottery workers ~xposed to sdica and talc. Am J EpiJemt<>l !981: 125:35-43.

96. Wilkm..<•Jn GS. Tie{! en GL. Wiggs LD. Galke W A, Acquavella )F. Rcye' '.1. \'c>d: GL. \X'axwcdler Rj. Morrality amnng plutonium and l>tr.er radianon workers at a plutonium weapons facihry. Am J EprJecc.1ol 1987:125:231-250.

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103. Stem FB, Beaumont JJ, Hal perm WE, Murthy LI. Hills BW. Fajen JM. Mortality of chrome leather tannery workers and chemical exposures in tanneries. Scand J Work En vir Health 1987; 13:108-117.

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119. Hogstedt C. Andersson K. Frenning B. Gustavsson A. A cohort ;:::dy of mortality among long-time employed Swedish chimney sweeps. Scand J Work Envir Health 1982;8(supl1):72-78.

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Appendix Associations between malignant (and other) disease SMRs and nonmalignant disease SMRs (all log-transformed) across the set of selected cohorts were estimated by least squares regression. The model assumed was as follows:

SMR(cause, ), =a,+ b, · SMR(all-nonmalignanr) +E.

where i = I, .. . n, and n is the number of cohorts analyzed;

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SMR(all-nonmalignant), and E, are random pairs that are independently and nonidentically distributed, that is, the SMRs from different cohorts have different expectations and vari­ances, reflecting cohort size and selection factors, and SMRs for disparate cause-of-death subsets are independent within a cohort.

This generalization from the usual linear regression model is nor common in epidemiologic research, although frequently appropriate in econometric problems. White has derived a consistent estimator fo r che variance of b having the form ( 122):

Epidemiology January 199!, Volume 2 Number 1

P~!RS AND S~!RS IN OCCUPATIONAL MORTALITY

"ar(b)

L (SMR(nonmalignant ), - SMR (nonmalignant) ,): · ~ :

( ~ (SMR(nonmalignant) , - SMR (nonmalignant)i ):

where e,! = (5\-IR(cause,), -a - b · SMR( nonmalignant) , hr) :, char IS, e, is the obse rved de\'iatilln of SMR (cause, ) from tts lease squares predicted \'alue .

j9