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Evaluation of multiple causes of death in occupational mortality studies
J Chron Dis 0 Pergamon
1978, Vol. 31. pp. 183-193 Press Ltd. Printed m Great
Britain
EVALUATION OF MULTIPLE CAUSES OF DEATH ZN OCCUPATIONAL MORTALITY STUDIES* OTTO CAROL *Dlvlsion International
WONG,? HOWARD E. ROCKETTE,J K. REDMONDJ and MARIAN HEIDg
of Biostatistlcs and Epidemiology, Department of Community Medicine Health. Georgetown Universitv School of Medicine. Washington. D.C. tDepar&&t :f Biostatistics, &aduate School of Public He&h, University of Pittsburgh. Pittsburgh. PA $Office of Health Surveillance and Biometry, National Institute for Occupational Safety and Health, Rockvllle, MD (&rewed
m revised form
7 November
and 20007
1976)
Abstract-An exploratory examination of the posslbllity of usmg multiple causes of death information from death certificates in mortahty studies has been undertaken. The formation of a matrix of primary by contributory causes has been suggested. The matrix gives an overall picture of mortality patterns. serves as a scheme to identify disease combmatlons, and can be utilized as a data-editing device. From the matrtx, several conventional, as well as several new statistics can be computed. The methodology has been illustrated with data from a cohort of steelworkers. The gain m mformation as a result of routinely recording multiple causes of death has also been discussed.
INTRODUCTION
The limitations of using primary cause of death to evaluate mortality have already been discussed in several papers [l&6]. These limitations can be crudely classified into two categories. The first consists of criticisms of mortality statistics in general. Thus, we may question the accuracy of clinical diagnosis, the ability to determine conditions of morbidity seldom associated with death, and the feasibility of attempting to associate death with a single cause. Although these limitations are important to recognize, they will not be given further attention here, and the interested reader should consult the previously referenced papers. The second group of criticisms are concerned with the lack of use of information already available on the death certificate. In addition to the primary (underlying) cause, the physician may also report ‘disease or condition directly leading to death’, ‘antecedent cause’ or ‘other significant conditions’. This type of information has seldom been used in analysis. In fact, the determination of the various uses of multiple causes of death data and an evaluation of the extent of their usefulness are still unresolved. Furthermore, it is seldom emphasized that the benefit of using multiple causes of death data to supplement the primary cause will depend upon the particular question of interest. Thus, if one wishes to determine to what extent a disease contributes to mortality, or obtain some estimate of the prevalence of a disease at death, it is obvious that diseases such as diabetes and hypertension will be under-represented if only the primary cause is considered. Alternatively, if we are comparing two groups to determine if they have similar mortality patterns, it is not clear what the effect of failing to consider multiple causes will be. If diseases such as diabetes and hypertension are under-represented by the same magnitude in both populations, comparisons based on only the primary causes of death may not be inadequate. *Address Rockville.
reprint requests to Dr. MD 20852 U S.A.
Wong,
Equitable
Environmental
Health,
Inc.,
6000
Executive
Blvd.,
1x4
OTTO WONG et al.
The main purpose of this paper is to discuss some possibie uses of causes of death data and some of the related problems encountered when ing mortality patterns of an occupational cohort to an appropriate control tion. In some cases, the extent of the usefulness of multiple causes will be fied by applying the techniques to a cohort of steelworkers.
multiple comparpopulaexempli-
Dejinition of the cohort Data presented in this report come from a longitudinal study of mortality in the steel industry conducted by the Department of Biostatistics, University of Pittsburgh, with funding from the U.S. Public Health Service and the American Iron and Steel Institute. Data collection and follow-up procedures have been described in earlier papers [7,8]. Brieffy, the cohort consisted of 58,828 steelworkers employed in 1953 at seven steel plants in Allegheny County, PA. The cohort represents approximately 62% of all men working in basic iron and steel production in the county in 1953. Information obtained from plant personnel records included a complete work-history which made possible the classification of steelworkers into different work areas at different points of time during their employment. In addition, all other basic data such as birthdate, race, as well as other identifying information useful for follow-up were included. For men who left employment before January 1, 1967, follow-up was done to determine vital status. Only 54 individuals (less than 0.1%) were lost to follow-up. For all decedents, copies of death certificates were obtained from the appropriate State offices of vital statistics. All the causes of death were coded by one of the authors (M.H.), a nosologist trained at the National Vital Statistics Division of the U.S. Public Health Service, according to the Seventh Revision of the International Classification of Diseases. The primary cause of death was chosen in accordance with the rules and guidelines given in the section “Medical Certification and Rules for Classification” in the Manual [9]. A complete description of the coding scheme in the study has been given by Appendix 1 in Redmond [lo]. One important supplement to the usual Seventh Revision Coding was the use of code 434.5 to designate car pulmonale. For the entire steelworker population, a total of 8628 deaths were observed during 1953-1966. In addition to the 8628 primary causes, 5127 contributory causes were coded. The number of diagnoses coded per death varied from one (the primary) to a maximum of seven. Methodology The most general approach to multiple causes of death classification would be to consider each possible disease combination as a separate category, with consideration being given both to the order in which the diseases were reported and the part of the certificate in which they are given. As Guralnick [l] has already stated, the tabulation and publication of such lists would be mechanically cumbersome and would result in extremely fractionated data. The latter would make this classification scheme particularly difficult to apply to most occupational cohorts. In addition to subdivisions based on age, race and sex (three factors all of which may affect the frequency of contributory causes of death present on the certificate), many occupational groups can be further subdivided by job category, length of exposure and type of exposure. Such further subdivision would result in numbers too small for interpretation in all but the largest occupational cohorts and the most prevalent disease combinations. A less cumbersome procedure which still retains some of the information on disease combinations, would be to obtain frequencies of the number of times a particular primary cause is associated with a contributory condition. This type of cross-tabulation, or matrix, can be further sub-divided by age, race and sex if the number of deaths is sufficiently large. Table 1 gives the cross-tabulation
causes
1
20
3 2
8
6
8
29
3
I
1
0
4
U 4
U 72
0 74
758
1 36
0 I1
15
183
Column total
Column totahdlagonal
1 37
622
537
I37
148
0 32
JY I
127
0 24
9 0
7
1s
P
1 0
9
6
5
1 385
8
3 0
17
2
2
153 1
2
8
3
5
0
2
0
1
0
6
2 61
164
23
0
1
9
!3
27
12 0
51
24
1 92
1135
U 56
81 3
!7
199
120 0
39
590
20
3 2
1
3
I
7
-
CONTRIBUTORY
101
Causes of death are coded accorchng to the 7th Revision of ICDA. Sum of diagonal elements = 7394 (Total number of deaths). * Columns have the same title as corresponding rows.
15
6X I
37 I
8 0
14
17
23
1
5
7
0 3
I 2
0 0
0
0
0
453
0
556
0
0
4
4
3
0
0
41
1
1 CROSS-.~ABULATIONOF PRIMARY BY
(260) 7 Vascular lesions of CNS (33&334) 8 Artenoscelerotlc heart disease (420-422) 9 Hypertensive heart dnease (44&447) excludmg COTpumonale 10 Cor pumonale (4345) 11 General arter!osclerosls (450) 12 Other cardiovascularrenal dlseasc 13 Non-mahgnant Respiratory disease 1470-527) 14 Ace,d& (W&962) Homlcldes and $uudes (963-964. 97&985) lb All other causes
3
Respiratory cancer (62-163) 4 Gemto-urmary cancer (177-181) 5 Other cancers 6 Diabetes melhtus
(15&159)
1 Respiratory tuberculosis (00LM110 2 Dlgestlve cancer
c ontrlhulory
TARLE
I 57
4585
0 ‘35
216 8
375
366
183 4
2927
III
101
15 11
8
18
7
I(
CAUSES
142
501
U 37
35 2
-19
36
207 I
4x
72
11
2 0
0
I
0
OF DEATH
267
x
0 2
I 0
0
0
0 3
1
0
0
I 94
97
0 8
13 2
Lb
SO
0 0
2
0
3
I 68
399
0 31
37 4
237
IX
8 1
32
17
2
0 2
1 1
I 0
0
4
0
12
2 00
505
0 57
252 5
44
19
4 47
47
13
OF WHITE
0
1
0
11
A COHORT
0
0
u
AMONG
1 I?
4x5
0 6
IO 430
i3
2
4 0
12
14
STEELWORKERS,
95
1
0
1 02
154
151 2
0 0
0
I 55
1330
0 X58
113 R
123
IX
33 IO
29
0
0 0
287
?I 0
I I672
151 14x4
‘)3X 46b
967
7lh
5x1 73
3334
882
185 416
4 4
0 0
472
600
4
4
0
60
Ro\, lotal
0
4
16
U
15
1953-1966
I 00 I 73
3 1’ 108
4”X
I? 5’
281 24 33
114
1 49
2 84
121 1 08
104
108
1 46
Ron total dla~onal
IX6
OTTO WONG et al.
of frequencies of death by primary and contributory causes among the white steelworkers. In constructing Table 1, diseases have been grouped into sixteen categories, and codes on a single death certificate belonging to the same category were counted only once. Thus, the selection of the categories will affect the total number of entries in the table. In Table 1, the diagonal elements represent the number of deaths, 1953-1966, whose primary causes are given by the column headings (or row headings). For example, there were 41 deaths with respiratory tuberculosis coded as the primary cause. The sum of the diagonal elements, 7394. is the total number of deaths observed in the white males. The off-diagonal elements represent the counts of deaths with the primary cause given by the column heading and one of the contributory causes by the row heading. Thus, among the 41 respiratory tuberculosis deaths, (i.e. deaths with respiratory tuberculosis listed as the primary cause), 1, diabetes mellitus, 4 arteriosclerotic heart disease. 4, car pumonale as contributory cause, etc. Column totals give the total number of conditions coded among those whose primary is given by the corresponding column heading. Thus, among 41 tuberculosis deaths, a total of 75 conditions was coded: 41 being the primary (tuberculosis), and the reamining 34 distributed among various contributory causes. On the other hand, row totals represent the number of times that a particular condition was coded either as the primary or as a contributory cause. For instance, tuberculosis was coded sixty times, of which 41 were coded as the primary and 19 as a contributory cause. The grand total, 11.672, given at the lower right corner, is the total number of frequencies (primary and contributory) coded among the sixteen disease categories for these 7394 deaths. Traditionally, most occupational mortality studies have been based on the diagonal elements only. Using non-malignant respiratory disease as an example, the usual procedure is to compare the observed number of 252 (the diagonal element) to some expected number derived from a control population. One possible improvement of this procedure is to make similar comparisons on off-diagonal elements. It may be of interest to compare the 47 non-malignant respiratory deaths with car pumonale as a contributory cause to a corresponding expected value based on a control population. In this case, the morbid condition at the time of death will be more adequately and more specifically described. This procedure of comparison may prove to be particularly interesting in certain combinations of diseases related to occupational exposures. Guralnick [l] suggested a 2 x 2 contingency table to separate those combinations which reflect concomitant disease processes from those that are occurring randomly. The pairs are divided into four cells as shown in Table 2. The entries for this 2 x 2 array are readily obtained from the matrix of values in Table 1. Assume that disease A and disease B represent the ith and jth diseases tabulated in Table 1. Then, a,, = entry in the ith column and jth row, a2r = diagonal element in the ith column minus a, 1, aI2 = jth row total minus all, and az2 = sum of all diagonal elements minus alr, azl, and a12. For example, the entries in the 2 x 2 table for diabetes as a primary cause and cerebral vascular lesions as a contributory cause would be: all = 24, a21 = 77, al2 = 858, and az2 = 6435. This would yield a ratio of observed to expected of 1.99 (x2 = 12.53, p < 0.001). This significant result shows clearly the association between diabetes as a primary cause and cerebral vascular lesions as a contributory cause. On the other hand, if we wish to compare disease combinations of two groups,
Evaluation
TABLE
of Multiple
2. 2 x 2 CONTINGENCY TORY
TABLE
Causes
BY
of Death
PRIMARY
AND
187
CONTRIBU-
CAUSES
we have to modify the 2 x 2 table as follows. The columns of the table would represent groups, and the rows would be disease A as the primary with B as a contributory and disease A as the primary without B mentioned on the death certificate. For example, we may be interested in comparing the proportions of deaths with cerebral vascular lesions as a contributory cause among those whose primary cause was diabetes. Tests for significance can be obtained using the chisquare or for small sample sizes Fisher’s exact test. One previously tabulated statistic that can be derived from Table 1 is the ratio of row total to the corresponding diagonal element, i.e. the ratio of the total number of times a particular cause is coded (sum of primary and contributory) to the number of cases in which the same cause appears as the primary cause alone. To facilitate discussion, the term ‘associated cause’ will be used to denote the sum of primary and contributory causes, i.e. the row totals. The computed values of this ratio of associated to primary cause for each of the sixteen causes of death are shown in Table 1. The ratios for digestive and respiratory cancers are 600/_556 = 1.08 and 472/453 = 1.04, respectively. In fact, except for genito-urinary cancer with a ratio of 1.21, the various cancers have ratios very close to unity. For homicides and suicides, the ratio is exactly one, indicating that this category had never been coded as a contributory cause. On the other hand, car pulmonale had a ratio of 24.33, general arteriosclerosis 15.52, non-malignant respiratory disease 3.72 and diabetes mellitus 2.84. A ratio substantially larger than unity means an under-reporting of conditions at death if analysis is based on the primary cause alone. However, if comparisons are being made between the mortality patterns of two groups, analysis based on the primary cause may still be adequate if the ratios in the two groups are the same. Several studies in the literature have tabulated this type of ratio for general populations [l, 2,6]. These ratios may, of course, be affected by a variety of factors. Coding differences, sex, age and race are among the more obvious causes of differences in this ratio between populations. Table 3 shows the ratios for the steelworker population for selected causes of death by age. and separately, by race. The ratio for all causes for those under 45 years of age was 1.55 while the ratio for those who were 45 or older was 1.87. Individual diseases may show even greater differences. The ratios for the two age groups are, 2.84 and 4.10 for influenza, 1.27 and 1.55 for cirrhosis of the liver, and 1.71 and 2.86 for diabetes respectively. Although an increase in the ratio with increasing age is most common, a decrease occurs for some diseases. The younger age group has a ratio of 34.50 in general arteriosclerosis and the older group, 14.36. This observation reflects the fact that although arteriosclerosis can appear early in life, it is not usually coded as the primary cause for younger age groups. The category ‘other hypertensive disease’ is the only other category in Table 3 that shows a slight decrease in the ratio with age. Race may also affect the value of the ratio. Table 3 also shows the ratio for whites and nonwhites for the steelworker cohort. The largest differences occur for diabetes (2.84 vs 1.74), the category ‘other hypertensive disease’ (8.34 vs 3.78), and cirrhosis of the liver (1.39 vs 2.09). General arteriosclerosis, nonmalignant respiratory disease and acute infections all show notable differences. In addition, most of the genito-urinary cancers reported as contributory causes occur in the
188
OTTO WONG ef al. TABLE 3. RATIO OF ASSOCIATED TO PRIMARY CAUSES OF DEATH AMONG THE 1953 STEELWORKER COHORT BY AGE ANDRACEFORSELECTED CAUSES OF DEATH
Cause
of death
All cauxs (001-999) Respiratory tuberculosis 1001-008) Dlgestlve cdncer (1X&159) Lung cancer (162-163) Gemto-urmary cancer (177-1811 Diabetes melhtus (260) Cardmvascular renal disease (33&3341. (4C@456), (592-594) Vascular lesmns of CNS (33&334) Arterlosclerotlc heart dtsease (426422) Hypertensne heart disease 1446443) Other hypertenswe dmzase (444-4471 General arterlosclerosls
than
5 coded
Race 45+
White
I55
187
179
1 79
140 I 04 I 03
I 46
I 46
1 10
104
1 10 n4
I ‘to 108
105 171
IL9 286
21 2 84
I
1 05 I 74
154
I81
I 73
I 90
160
I 66
I 64
1 72
NonwhIte
I
1 02
I 12
1 20
117
1 27
1 66
1 77
181
1 56
6.90 34 50’
(450) Nonmahgnant respxitory dmzase 147&5271 Acute mfectmns (470-493) Ulcer of stomach (540) Curhos,s of liver (581)
*Less
Age
<45
3 2 1 1
75 84 63 27
665
8 34
3 78
1436
15 46
I? 29
415 4 IO 163 155
4 27 4 05 57 39
3 7 2 2
I I
27 90 33’ 09
for the primary cause.
white population. Further subdivision by age and race categories showed that for most diseases the trend of the ratio by age was the same for both races. The one exception was nonmalignant respiratory disease where the whites had a ratio of 3.70 and 4.41 in the two age groups respectively, and the nonwhites had ratios of 3.92 and 3.10. Although comparisons by sex were not possible for the steelworker study, this factor has been shown to have an effect on the ratio. Olsen et aE. [Z] have slightly, but consistently higher ratios for women than for men in the category of all causes for all age groups. Although many factors may affect the value of the ratio, the ratios for different groups are similar for most diseases, even when compared across studies. Table 4 shows the values of the ratio for selected causes as reported by Guralnick [l] for a one third sample of deaths occurring in the U.S.A. during 1955, and by Olsen et al. [2] for a 50% sample in the State of California during the same year. Also included in the table are the ratios for the entire steelworker cohort. Although no adjustments have been made for sex, age and race, the results from these three studies are quite similar. The lower value for the steelworkers in “all causes”, and several of the cardiovascular categories may be indicative of the fact TABLE
4.
COMPARISON
OF
ASSOCIATED
SELECTED
CAUSES
TO
PRIMARY
AMONG
THREE
CAUSES
OF
DEATH
Steelworkel study*
GLWhXk’S
Cause
of death
study
R&ratory cancer (162-163) Diabetes melhtus (260) Vascular lessons of CNS (330-334) Arteriosclermtlc and degenzratwe heart disease (42W422) Hypertensive dwase W&447) General artermsclerosls (450) Nonmahgnant resputory disease (478-527) Curhow of the hver (581) Accidents (80&962)
Causes NA
of death are coded according available. white and non-white.
= Not
*Both
to
RATIOS
STUDIES
I91 105 104 246 1 75
2 05 1 08 104 3 18 1 95
1 58 I 08 104 261 151
132 2 50 7 50
1 34 2 69 NA
I IS 7 84 1507
3 79 I51 1 22
4 46 NA 118
321 143 108
the 7th Revision
of ICDA.
FOR
Evaluation of Multiple Causes of Death
189
that it is a younger population. The most striking difference is for general arteriosclerosis (450). The ratio for the steelworkers study is nearly twice as large as the one reported by Guralnick. The discrepancy is mostly due to a coding difference. In the steelworkers study, whenever 422.1 appears as the primary, 450 is automatically coded as a contributory cause of death. If the investigator finds it necessary to make comparisons among different groups where the coding rules used by the nosologists were not the same, a detailed examination of these ratios may be useful. In fact, any comparisons on primary causes between two groups when the ratios of associated to primary causes differ, should be examined closeiy. If the difference in the ratios is due to a factor for which an adjustment has been made in the comparison of primary causes (age, sex, etc), then the discrepancy may be due to the effect of these factors on the unadjusted ratio. However, if the difference in the ratio is due to a failure of a condition to be reported as a primary cause, misleading interpretations may result. One example of what may occur is illustrated by digestive cancers among the steelworkers in the mason department [12]. For the white mason workers, the expected number of digestive cancer was 9.0 compared to an observed of 3.0, with a relative risk (the MantelHaenszel summary Chi-square procedure [13]) of 0.33 (11< 0.10). Because of the small numbers, and the borderline significance, no strong statements could be made and the results were considered only suggestive. However, the suggestive nature of the data becomes weaker when one notes the ratio of 1.67 for stomach cancer in the white mason workers. Two additional death certificates mentioned stomach cancer, but the latter was not coded as the primary cause. For general populations or all steelworkers, less than 5:/d of digestive cancers appear as a contributory cause, but for the mason workers, there were 40?.. However. one must interpret this result with caution, since the numbers are fairly small. One possible way in which multiple causes of death might be used is to tabulate the contributory causes in the same way that the primary causes have been used. Thus, we evaluate mortality using the number of certificates that have a particular cause mentioned, either as a primary or contributory cause. As mentioned earlier, the term ‘associated cause of death’ has been suggested as a measure of prevalence at the time of death, and has already been used to evaluate the steelworker data [lo]. It is interesting to compare the results of the analysis using only the primary cause with those using associated cause of death. A comparison was made of the causes of death which were indicated as having high relative risks (p < 0.05) by either method in one of the 57 work areas defined in the study. For the purpose of this study, the first job in 1953 determines one’s work area, and all other areas are used as the control [14]. Evaluations were based on the 27 causes of death outlined in the report [lo] in addition to diabetes (260.0) and hypertensive heart disease (44&447). Comparisons were made for all possible combinations generated by race, work area and cause of death. Fifty-four of these race-area-cause combinations were identified to have a significantly high relative risk by either or both approaches. Of these, 27 were found to be significantly high using both methods of analysis. In this report, we would be more interested in those identified by only one method. Table 5 shows the observed and expected deaths, and relative risks using both methods of analysis for the work areas and causes of death for which the relative risk based on primary cause was significantly high, but the relative risk based on associated cause was not. Of the 9 cases where this occurred, 2 were in the categories of cerebral vascular lesions and hypertensive disease. Two of the discrepancies were in remainder categories (‘all other cardiovascular renal disease’ and ‘all other causes’). Significance in these nonspecific categories is always difficult to interpret and since for these two work areas a further subdivision did not show more specific findings, no further commem will be made. The loss of significance for all cancers among the white ‘general technical’ workers is not the result of a large change in relative risk, but rather a small shift across the
OTTO WONG et al
190 TABLE 5. CAUSESOF DEATH BY WORK CAUSE WAS SIGNIFICANTLY HIGH
Work
area
G.Xler.Xl techmcal Coke plant Machme shop Weld shop Sheet fimshmg shlppmg El.XtrlC furnace Billet. bloom & slab mills Anneahng. normallzlng
Continuous ptcklmg electrIca
Race
Cause
of death*
AREA AND
AND
RACE
ANALYSIS
FOR WHICH
BASED
ON
ANALYSIS BASEDON
ASSOCIATED
CAUSE
PRIMARY
WAS
NOT
Obs
White White White Whlre
(14&?05) (177-181) (33&334) (330-334)
62 9 31 I1
46 8 3 84 19 I 53
White
(444-468, (592-594)
14
Wh,te
(44&4J7)
White
(44&G&7)
21
White
(47&527)
14
White
all other causes 1
5
5
I 2 I ?
35: X4$ 688 1st:
62 9 38 II
49 7 50 28 7 78
I27 202 I 35 43
72
2 35b
52
41 6
I 32
14
4 67b
5
J6
I 10
II 4
2 osa
42
34 b
125
74
2 ClSt.
34
27
I 30
13
-I 31:
I
and
cleanmg
* ICDA, 7th Revision. t Significance of relative risk based on Chl-square with $ Significant at 5’6 level. $ Significant at 14; level. // Causes excludmg respiratory tuberculosis (OOl&OOS), all renal disease (330-334, 400468. 592-294). non-malignant rhosis of liver (580), accidents (800-962). horrucides and ill-defined causes (780-795).
6
one degree
26
244
of freedom.
cancers {14&205), respiratory &ease suicides (963-964,
cardiovascular(470-537). clr97%985), and
arbitrary p = 0.05 boundary. Several of these mmor changes will occur whenever a comparison of this type is made. The annealing normalizing work area showed a change in significance for the category nonmalignant respiratory diseases, but more detailed subdivision within the category was not possible because of the small frequencies. The final discrepancy is of particular interest since it concerns one of the more important findings in regard to previous analysis of the steelworkers cohort. The high kidney cancer in coke oven workers has already been noted [lSj. In this case, the loss of significance when prevalence as indicated by associated cause was evaluated is due to the disproportionately high number of kidney cancers within the urino-genitary cancer category, as compared to the entire steelworker cohort as a whole. Table 6 shows the 17 cases for which the relative risk based on prevalence was significant, but the relative risk using primary cause faiied to be significant. From these 17 cases, 6 were ones for which there were less than 5 observed deaths when analysis was based on primary causes and thus no relative risk was computed. Four of the cases are in nonspecific remainder categories. Five others show only a slight shift in the relative risk and the resulting significance is due mainly to the increased sample size when prevalence is used in the evaluation. The major finding is the lung cancer in the white machine shop workers. This was suggestive when using analysis based on primary cause, but with analysis based on prevalence this could have been anticipated it became significant at p < 0.01. However, because
of the
for
department
this
higher
ratio
of
associated
to
primary
causes
of death
in lung
cancer
(26/21 = 1.24). DISCUSSION
Some of the problems encountered in using multiple causes of death data in occupational mortality studies have been discussed. Some suggestions have been made and in some cases evaluated using a population of steelworkers. The relevance of using this type of data to evaluate mortality patterns in occupational cohorts has been emphasized. The study of individual disease combinations is the most general approach to the analysis of ‘multiple cause’ data, but unfortunately the number of possibilities
Evaluation
6. CAUSES OF DEATH
TABLE
my WORK
SIGNIFICANTLY
HIGH
of Multiple
AREA AND AND
Causes
RACE FOR
ANALYSIS
of Death
ANALYSIS BASED ON
WHICH
BASED
191
ON
PRIMARY
CAUSE
WAS
ASSOCIATED
Transportatmn and yard Black\mlth shop MachIne shoe Macon dept Machmc shop Gellerdl techmwl StaInlea ulnealmg pxhhng and procesr1ng Heat treatme and forge Janitors
Jamtors Plant protectlo” Sheet fimshmg and shlppmg General techmcal Pipe shop Plant protectmn Ran1 mill fimshiog Annealmg normallzlng Jamtors
* i 1 $ ~
area
Cause
of death’
Wh,te
1150 ISYI
White White White Wh,tu
(16&1641 IlhO-1641 ilb?-163, (I62-1631 (165. 170 192-1991
Wh,te
(165. 170 192. 1991 (33lK334) (400 4561 (592 -594) (33&3341 (40s 456) 1592-5941 i42&42?1 (4l%419, (423-443,
White
NonwhIte White
White White
(4O&ll9) (423 443) (444-468) (592-5941 (44&4681 1592-5941 i44&46Xi (593-5941
Nonwhlie White Whnte
White NonwhIte White White
ICDA 7th Revision. Slgmficance of Relative Significant at 5”/, level. Significant at 1’; level. Less than 5 deaths.
Risk
based
Ohs
EXP
Rlakt
Obs
E~P
Rel
Rlskt
I39
I 55
153
1 61:
21 13 IX
32 I42 73 135
2 13 I51 Xl
I I 3h
34 150 77 141
242t I 79b 86: I 68$
I IX
25 5
I 441
I I
5 376
51
01
I
14
I6 83
I 76
6 20
19 II8
3 70+ I 791
32
32 I
I 37
50
366
I 45:
IO
5x
I 76
28
186
I 54:
0
04
I6
6 42~
69 31
IO1 64
I
55 18
37 3 19 1
I 539 I 50$
41
‘01
35
24 7
146:
2x
3 34:
10 63
3 62: 202
1470.527)
I6
(001-008) (44&447)
11 1’)
on Chl-square
Rel
21
6
with
one degree
WAS
Assoaated
Pnmary Work
CAUSE
NOT
6 I?
of freedom.
results in extremely fractionated data. A compromise between the desire to incorporate contributory causes in the analysis and to keep the total number of classifications manageable is to use a cross-tabulation of primary by contributory causes. Analysis can be based on the individual cells of the matrix in a similar manner to the usual evaluation of primary cause only. Although analysis based on the individual cells may provide useful information in regard to disease combinations, several problems are encountered. First, similar cross-tabulations for the total U.S. and other standard control groups have not, in general, been produced, one exception being for the year 1955 [ll]. Thus, unless an internal comparison is being made, there is no way to obtain an expected number for some of the cells. Second, if further subdivisions are made on the basis of age, sex and race, the numbers for many disease combinations may be too small for reliable statistical interpretation. In spite of these problems, the matrix is beneficial to tabulate. First, it provides an additional method of editing the data. Thus, improbable combinations can be rechecked for possible abstracting or coding errors. In addition this type of tabulation gives some insight into which diseases within the cohort are associated with one another. This type of information may be useful in determining which disease combinations should be tabulated for standard populations. Finally, in the case of more frequently occurring combinations, internal comparisons or comparisons with other study groups could be made, thus refining the analysis presently used to study differences in mortality patterns of two working groups. An additional advantage of this type of tabulation is that it incorporates many previously used statistics as special cases of this generalized concept. The frequency
192
OTTO WONG et al.
of the primary cause of death, the ratio of associated to primary frequencies for a particular cause and the 2 x 2 contingency tables suggested by Guralnick are all definable in terms of this matrix. Additional examples are readily available. The ratio of the diagonal element to the difference of the column total and diagonal element has been tabulated by others [l, 2-J. A ranking of these proportions will indicate which contributory causes are most often associated with particular primary causes. For example, among the 252 deaths with nonmalignant respiratory disease listed as the primary cause, (505-252) or 253 contributory causes were coded. Arteriosclerotic heart disease (47/253) and car pulmonale (47/253) are most often coded among these contributory causes. Another widely used statistic is the PMR (Proportional Mortality Ratio). The ratio of the diagonal entry to the sum of the diagonal elements is equivalent to a proportional mortality ratio where no adjustment has been made for age. This ratio can be age-adjusted by using similar ratios from some standard populations. An investigation was made to evaluate the extent of usefulness of two of the statistics employing multiple causes of death data. First, the ratio of associated to primary causes of death was evaluated in detail. Although the ratio does vary for different age, race and sex groups, it is similar for many diseases even between various studies. One possible use might be to compare the ratios before contrasting mortality patterns of sets of data where the rules used by the nosologists may differ. Likewise, it may be used to screen for situations where comparisons based on the primary cause would be misleading. This is particularly useful in those cases where there are not many observed deaths, thus a larger than expected number coded as a contributory rather than a primary cause could easily affect the result. Again it is emphasized that a ratio different from one does not necessarily indicate an elevation or a deficit in mortality risk, but a difference in the ratios between two groups being compared should deserve further attention. A second statistic evaluated in detail is the ‘prevalence’ of the disease of the associated cause of death on the death certificate. Comparisons of results of analysis based on this statistic with those based on primary cause of death indicated that the gain in information is not as large as one might anticipate. In fact the increase in sensitivity appears to be due mostly to an increase in sample size thus resulting in an increased number of statistically significant results. In addition, discrepancies in the two methods seemed to occur mostly for cerebral vascular lesions, hypertensive disease or nonspecific remainder categories. Although the multiple cause procedures presented here are useful in describing contributory causes, and in a few instances have resulted in improved interpretation of the data, the increased amount of information obtained when comparing an occupational cohort with another population has not been large. This suggests that the additional effort necessary to incorporate multiple causes of death data into a study of this type may not be necessary. Three qualifications to this statement must be reiterated. First, this does not apply to the use of multiple causes of death data in situations other than those described here. Second, evaluations were based on the steelworker population. Although the gain in information using multiple cause data was slight for this group, this may not be true for other populations. Finally, the more complicated problem of using certain disease combinations has been suggested, and, if frequencies permit, should be dealt with more completely in the future.
SUMMARY
(1) The formulation of a matrix of primary by contributory causes serves to give an overall picture of the mortality pattern of an occupational group and has the additional advantage of being useful as an editing device. If tabulations also exist for an appropriate control, comparisons may be made for disease com-
Evaluation
of Multiple
Causes
of Death
193
binations of interest. If frequencies are large enough, adjustments can readily be made for age, sex and race. (2) A comparison of the proportion of deaths from a particular primary cause with a particular contributory cause mentioned on the death certificate can be obtained. This is a refinement of the current practice in comparing mortality patterns. (3) The ratio of associated to primary causes of death may be used to determine if results based on the primary cause are misleading. It may also be used prior to comparison as a screening device to determine if the coding schemes between two different studies are comparable. In general, however, this ratio is not very sensitive to the type of populations being compared. (4) For the steelworker population, analysis based on associated cause yields only a slight gain in information as compared to that based on the primary cause alone. Although it is clear that multiple causes of death coding is necessary in certain applications, it is not clear that the gain in information is worth the effort of routinely coding and analyzing multiple causes of death in every study. Two possible exceptions are when the disease is usually only present as a contributory cause (for example, car pulmonale) or when special disease combinations are of interest. REFERENCES
t 2. 3. 4. 5. 6. 7. 8. 9 10. il. 12. 13. 14 15.
Guralnick L: Some problems in the use of multiple causes of death. J Chron Dis 19: 979-990, 1966 Olson FE, Norris FD, Hammes LM, Shipley PW: A study of multiple causes of death in Californib. J Chron Dis 15: 157-170, 1962 Dorn HF, Moriyama IM: Use and significance of multiple cause tabulations for mortality studies. J Am PuW Hlth Ass 54: 40%406, 1964 Markush RE, Siegal DG: Prevalence at death-l. A new method for deriving death rates for specific disease. J Am Pub1 Hlth Ass 58: 544-557, 1968 Krueger DE: New numerators for old denommators-Multiple causes of death. Nat Cancer Inst Monograph 19: 431-3, 1966 Cohen J. Steinitz R: Underlying and contributory causes of death of adult males m two districts. J Chron Dis 22: 17-24, 1969 Lloyd JW, Ciocco A: Long-term mortality study of steelworkers-I Methodology. J Occup Med 11: 299-310, 1969 Redmond CK, Snuth EM, Lloyd JW, Rush HW: Long-term mortality study of steelworkers-3. Follow-up. J Occup Med 11: 513-521, 1969 Manual of the International Statistical Classification of Disease: Injuries and cause of death WHO. 7th Revision, Geneva, 1957 Redmond CK: Comparative cause-specific mortality patterns by work area withm the steel mdustry. HEW Publ. No. (NIOSH) 75-157, 1975 National Center for Health Statistics: Vital statrstlcs instruction manual-2. Cause-of-death coding. 1955 Rockette HE, Redmond CK: Long-term mortality study of steel-workers-10. Mortality patterns among masons. J Occup Med 18: 541-545. August 1976 Mantel N, Haenszel W: Statistical aspects of the analysis of data from retrospective studies of disease. J natn Cancer Inst 32: 719-248. 1959 Lloyd JW. Lundin FE, Redmond CK. Geiser PB: Long-term mortahty study of steelworkers-4. Mortality by work area. J Occup Med 12: 151-157. 1970 Redmond CK, CIOCCO A, Lloyd JW, Rush HW: Long-term mortality study of steelworkers4. Mortality from malignant neoplasms among coke plant workers. J Occup Med 14: 621-629. 1972
1978, Vol. 31. pp. 183-193 Press Ltd. Printed m Great
Britain
EVALUATION OF MULTIPLE CAUSES OF DEATH ZN OCCUPATIONAL MORTALITY STUDIES* OTTO CAROL *Dlvlsion International
WONG,? HOWARD E. ROCKETTE,J K. REDMONDJ and MARIAN HEIDg
of Biostatistlcs and Epidemiology, Department of Community Medicine Health. Georgetown Universitv School of Medicine. Washington. D.C. tDepar&&t :f Biostatistics, &aduate School of Public He&h, University of Pittsburgh. Pittsburgh. PA $Office of Health Surveillance and Biometry, National Institute for Occupational Safety and Health, Rockvllle, MD (&rewed
m revised form
7 November
and 20007
1976)
Abstract-An exploratory examination of the posslbllity of usmg multiple causes of death information from death certificates in mortahty studies has been undertaken. The formation of a matrix of primary by contributory causes has been suggested. The matrix gives an overall picture of mortality patterns. serves as a scheme to identify disease combmatlons, and can be utilized as a data-editing device. From the matrtx, several conventional, as well as several new statistics can be computed. The methodology has been illustrated with data from a cohort of steelworkers. The gain m mformation as a result of routinely recording multiple causes of death has also been discussed.
INTRODUCTION
The limitations of using primary cause of death to evaluate mortality have already been discussed in several papers [l&6]. These limitations can be crudely classified into two categories. The first consists of criticisms of mortality statistics in general. Thus, we may question the accuracy of clinical diagnosis, the ability to determine conditions of morbidity seldom associated with death, and the feasibility of attempting to associate death with a single cause. Although these limitations are important to recognize, they will not be given further attention here, and the interested reader should consult the previously referenced papers. The second group of criticisms are concerned with the lack of use of information already available on the death certificate. In addition to the primary (underlying) cause, the physician may also report ‘disease or condition directly leading to death’, ‘antecedent cause’ or ‘other significant conditions’. This type of information has seldom been used in analysis. In fact, the determination of the various uses of multiple causes of death data and an evaluation of the extent of their usefulness are still unresolved. Furthermore, it is seldom emphasized that the benefit of using multiple causes of death data to supplement the primary cause will depend upon the particular question of interest. Thus, if one wishes to determine to what extent a disease contributes to mortality, or obtain some estimate of the prevalence of a disease at death, it is obvious that diseases such as diabetes and hypertension will be under-represented if only the primary cause is considered. Alternatively, if we are comparing two groups to determine if they have similar mortality patterns, it is not clear what the effect of failing to consider multiple causes will be. If diseases such as diabetes and hypertension are under-represented by the same magnitude in both populations, comparisons based on only the primary causes of death may not be inadequate. *Address Rockville.
reprint requests to Dr. MD 20852 U S.A.
Wong,
Equitable
Environmental
Health,
Inc.,
6000
Executive
Blvd.,
1x4
OTTO WONG et al.
The main purpose of this paper is to discuss some possibie uses of causes of death data and some of the related problems encountered when ing mortality patterns of an occupational cohort to an appropriate control tion. In some cases, the extent of the usefulness of multiple causes will be fied by applying the techniques to a cohort of steelworkers.
multiple comparpopulaexempli-
Dejinition of the cohort Data presented in this report come from a longitudinal study of mortality in the steel industry conducted by the Department of Biostatistics, University of Pittsburgh, with funding from the U.S. Public Health Service and the American Iron and Steel Institute. Data collection and follow-up procedures have been described in earlier papers [7,8]. Brieffy, the cohort consisted of 58,828 steelworkers employed in 1953 at seven steel plants in Allegheny County, PA. The cohort represents approximately 62% of all men working in basic iron and steel production in the county in 1953. Information obtained from plant personnel records included a complete work-history which made possible the classification of steelworkers into different work areas at different points of time during their employment. In addition, all other basic data such as birthdate, race, as well as other identifying information useful for follow-up were included. For men who left employment before January 1, 1967, follow-up was done to determine vital status. Only 54 individuals (less than 0.1%) were lost to follow-up. For all decedents, copies of death certificates were obtained from the appropriate State offices of vital statistics. All the causes of death were coded by one of the authors (M.H.), a nosologist trained at the National Vital Statistics Division of the U.S. Public Health Service, according to the Seventh Revision of the International Classification of Diseases. The primary cause of death was chosen in accordance with the rules and guidelines given in the section “Medical Certification and Rules for Classification” in the Manual [9]. A complete description of the coding scheme in the study has been given by Appendix 1 in Redmond [lo]. One important supplement to the usual Seventh Revision Coding was the use of code 434.5 to designate car pulmonale. For the entire steelworker population, a total of 8628 deaths were observed during 1953-1966. In addition to the 8628 primary causes, 5127 contributory causes were coded. The number of diagnoses coded per death varied from one (the primary) to a maximum of seven. Methodology The most general approach to multiple causes of death classification would be to consider each possible disease combination as a separate category, with consideration being given both to the order in which the diseases were reported and the part of the certificate in which they are given. As Guralnick [l] has already stated, the tabulation and publication of such lists would be mechanically cumbersome and would result in extremely fractionated data. The latter would make this classification scheme particularly difficult to apply to most occupational cohorts. In addition to subdivisions based on age, race and sex (three factors all of which may affect the frequency of contributory causes of death present on the certificate), many occupational groups can be further subdivided by job category, length of exposure and type of exposure. Such further subdivision would result in numbers too small for interpretation in all but the largest occupational cohorts and the most prevalent disease combinations. A less cumbersome procedure which still retains some of the information on disease combinations, would be to obtain frequencies of the number of times a particular primary cause is associated with a contributory condition. This type of cross-tabulation, or matrix, can be further sub-divided by age, race and sex if the number of deaths is sufficiently large. Table 1 gives the cross-tabulation
causes
1
20
3 2
8
6
8
29
3
I
1
0
4
U 4
U 72
0 74
758
1 36
0 I1
15
183
Column total
Column totahdlagonal
1 37
622
537
I37
148
0 32
JY I
127
0 24
9 0
7
1s
P
1 0
9
6
5
1 385
8
3 0
17
2
2
153 1
2
8
3
5
0
2
0
1
0
6
2 61
164
23
0
1
9
!3
27
12 0
51
24
1 92
1135
U 56
81 3
!7
199
120 0
39
590
20
3 2
1
3
I
7
-
CONTRIBUTORY
101
Causes of death are coded accorchng to the 7th Revision of ICDA. Sum of diagonal elements = 7394 (Total number of deaths). * Columns have the same title as corresponding rows.
15
6X I
37 I
8 0
14
17
23
1
5
7
0 3
I 2
0 0
0
0
0
453
0
556
0
0
4
4
3
0
0
41
1
1 CROSS-.~ABULATIONOF PRIMARY BY
(260) 7 Vascular lesions of CNS (33&334) 8 Artenoscelerotlc heart disease (420-422) 9 Hypertensive heart dnease (44&447) excludmg COTpumonale 10 Cor pumonale (4345) 11 General arter!osclerosls (450) 12 Other cardiovascularrenal dlseasc 13 Non-mahgnant Respiratory disease 1470-527) 14 Ace,d& (W&962) Homlcldes and $uudes (963-964. 97&985) lb All other causes
3
Respiratory cancer (62-163) 4 Gemto-urmary cancer (177-181) 5 Other cancers 6 Diabetes melhtus
(15&159)
1 Respiratory tuberculosis (00LM110 2 Dlgestlve cancer
c ontrlhulory
TARLE
I 57
4585
0 ‘35
216 8
375
366
183 4
2927
III
101
15 11
8
18
7
I(
CAUSES
142
501
U 37
35 2
-19
36
207 I
4x
72
11
2 0
0
I
0
OF DEATH
267
x
0 2
I 0
0
0
0 3
1
0
0
I 94
97
0 8
13 2
Lb
SO
0 0
2
0
3
I 68
399
0 31
37 4
237
IX
8 1
32
17
2
0 2
1 1
I 0
0
4
0
12
2 00
505
0 57
252 5
44
19
4 47
47
13
OF WHITE
0
1
0
11
A COHORT
0
0
u
AMONG
1 I?
4x5
0 6
IO 430
i3
2
4 0
12
14
STEELWORKERS,
95
1
0
1 02
154
151 2
0 0
0
I 55
1330
0 X58
113 R
123
IX
33 IO
29
0
0 0
287
?I 0
I I672
151 14x4
‘)3X 46b
967
7lh
5x1 73
3334
882
185 416
4 4
0 0
472
600
4
4
0
60
Ro\, lotal
0
4
16
U
15
1953-1966
I 00 I 73
3 1’ 108
4”X
I? 5’
281 24 33
114
1 49
2 84
121 1 08
104
108
1 46
Ron total dla~onal
IX6
OTTO WONG et al.
of frequencies of death by primary and contributory causes among the white steelworkers. In constructing Table 1, diseases have been grouped into sixteen categories, and codes on a single death certificate belonging to the same category were counted only once. Thus, the selection of the categories will affect the total number of entries in the table. In Table 1, the diagonal elements represent the number of deaths, 1953-1966, whose primary causes are given by the column headings (or row headings). For example, there were 41 deaths with respiratory tuberculosis coded as the primary cause. The sum of the diagonal elements, 7394. is the total number of deaths observed in the white males. The off-diagonal elements represent the counts of deaths with the primary cause given by the column heading and one of the contributory causes by the row heading. Thus, among the 41 respiratory tuberculosis deaths, (i.e. deaths with respiratory tuberculosis listed as the primary cause), 1, diabetes mellitus, 4 arteriosclerotic heart disease. 4, car pumonale as contributory cause, etc. Column totals give the total number of conditions coded among those whose primary is given by the corresponding column heading. Thus, among 41 tuberculosis deaths, a total of 75 conditions was coded: 41 being the primary (tuberculosis), and the reamining 34 distributed among various contributory causes. On the other hand, row totals represent the number of times that a particular condition was coded either as the primary or as a contributory cause. For instance, tuberculosis was coded sixty times, of which 41 were coded as the primary and 19 as a contributory cause. The grand total, 11.672, given at the lower right corner, is the total number of frequencies (primary and contributory) coded among the sixteen disease categories for these 7394 deaths. Traditionally, most occupational mortality studies have been based on the diagonal elements only. Using non-malignant respiratory disease as an example, the usual procedure is to compare the observed number of 252 (the diagonal element) to some expected number derived from a control population. One possible improvement of this procedure is to make similar comparisons on off-diagonal elements. It may be of interest to compare the 47 non-malignant respiratory deaths with car pumonale as a contributory cause to a corresponding expected value based on a control population. In this case, the morbid condition at the time of death will be more adequately and more specifically described. This procedure of comparison may prove to be particularly interesting in certain combinations of diseases related to occupational exposures. Guralnick [l] suggested a 2 x 2 contingency table to separate those combinations which reflect concomitant disease processes from those that are occurring randomly. The pairs are divided into four cells as shown in Table 2. The entries for this 2 x 2 array are readily obtained from the matrix of values in Table 1. Assume that disease A and disease B represent the ith and jth diseases tabulated in Table 1. Then, a,, = entry in the ith column and jth row, a2r = diagonal element in the ith column minus a, 1, aI2 = jth row total minus all, and az2 = sum of all diagonal elements minus alr, azl, and a12. For example, the entries in the 2 x 2 table for diabetes as a primary cause and cerebral vascular lesions as a contributory cause would be: all = 24, a21 = 77, al2 = 858, and az2 = 6435. This would yield a ratio of observed to expected of 1.99 (x2 = 12.53, p < 0.001). This significant result shows clearly the association between diabetes as a primary cause and cerebral vascular lesions as a contributory cause. On the other hand, if we wish to compare disease combinations of two groups,
Evaluation
TABLE
of Multiple
2. 2 x 2 CONTINGENCY TORY
TABLE
Causes
BY
of Death
PRIMARY
AND
187
CONTRIBU-
CAUSES
we have to modify the 2 x 2 table as follows. The columns of the table would represent groups, and the rows would be disease A as the primary with B as a contributory and disease A as the primary without B mentioned on the death certificate. For example, we may be interested in comparing the proportions of deaths with cerebral vascular lesions as a contributory cause among those whose primary cause was diabetes. Tests for significance can be obtained using the chisquare or for small sample sizes Fisher’s exact test. One previously tabulated statistic that can be derived from Table 1 is the ratio of row total to the corresponding diagonal element, i.e. the ratio of the total number of times a particular cause is coded (sum of primary and contributory) to the number of cases in which the same cause appears as the primary cause alone. To facilitate discussion, the term ‘associated cause’ will be used to denote the sum of primary and contributory causes, i.e. the row totals. The computed values of this ratio of associated to primary cause for each of the sixteen causes of death are shown in Table 1. The ratios for digestive and respiratory cancers are 600/_556 = 1.08 and 472/453 = 1.04, respectively. In fact, except for genito-urinary cancer with a ratio of 1.21, the various cancers have ratios very close to unity. For homicides and suicides, the ratio is exactly one, indicating that this category had never been coded as a contributory cause. On the other hand, car pulmonale had a ratio of 24.33, general arteriosclerosis 15.52, non-malignant respiratory disease 3.72 and diabetes mellitus 2.84. A ratio substantially larger than unity means an under-reporting of conditions at death if analysis is based on the primary cause alone. However, if comparisons are being made between the mortality patterns of two groups, analysis based on the primary cause may still be adequate if the ratios in the two groups are the same. Several studies in the literature have tabulated this type of ratio for general populations [l, 2,6]. These ratios may, of course, be affected by a variety of factors. Coding differences, sex, age and race are among the more obvious causes of differences in this ratio between populations. Table 3 shows the ratios for the steelworker population for selected causes of death by age. and separately, by race. The ratio for all causes for those under 45 years of age was 1.55 while the ratio for those who were 45 or older was 1.87. Individual diseases may show even greater differences. The ratios for the two age groups are, 2.84 and 4.10 for influenza, 1.27 and 1.55 for cirrhosis of the liver, and 1.71 and 2.86 for diabetes respectively. Although an increase in the ratio with increasing age is most common, a decrease occurs for some diseases. The younger age group has a ratio of 34.50 in general arteriosclerosis and the older group, 14.36. This observation reflects the fact that although arteriosclerosis can appear early in life, it is not usually coded as the primary cause for younger age groups. The category ‘other hypertensive disease’ is the only other category in Table 3 that shows a slight decrease in the ratio with age. Race may also affect the value of the ratio. Table 3 also shows the ratio for whites and nonwhites for the steelworker cohort. The largest differences occur for diabetes (2.84 vs 1.74), the category ‘other hypertensive disease’ (8.34 vs 3.78), and cirrhosis of the liver (1.39 vs 2.09). General arteriosclerosis, nonmalignant respiratory disease and acute infections all show notable differences. In addition, most of the genito-urinary cancers reported as contributory causes occur in the
188
OTTO WONG ef al. TABLE 3. RATIO OF ASSOCIATED TO PRIMARY CAUSES OF DEATH AMONG THE 1953 STEELWORKER COHORT BY AGE ANDRACEFORSELECTED CAUSES OF DEATH
Cause
of death
All cauxs (001-999) Respiratory tuberculosis 1001-008) Dlgestlve cdncer (1X&159) Lung cancer (162-163) Gemto-urmary cancer (177-1811 Diabetes melhtus (260) Cardmvascular renal disease (33&3341. (4C@456), (592-594) Vascular lesmns of CNS (33&334) Arterlosclerotlc heart dtsease (426422) Hypertensne heart disease 1446443) Other hypertenswe dmzase (444-4471 General arterlosclerosls
than
5 coded
Race 45+
White
I55
187
179
1 79
140 I 04 I 03
I 46
I 46
1 10
104
1 10 n4
I ‘to 108
105 171
IL9 286
21 2 84
I
1 05 I 74
154
I81
I 73
I 90
160
I 66
I 64
1 72
NonwhIte
I
1 02
I 12
1 20
117
1 27
1 66
1 77
181
1 56
6.90 34 50’
(450) Nonmahgnant respxitory dmzase 147&5271 Acute mfectmns (470-493) Ulcer of stomach (540) Curhos,s of liver (581)
*Less
Age
<45
3 2 1 1
75 84 63 27
665
8 34
3 78
1436
15 46
I? 29
415 4 IO 163 155
4 27 4 05 57 39
3 7 2 2
I I
27 90 33’ 09
for the primary cause.
white population. Further subdivision by age and race categories showed that for most diseases the trend of the ratio by age was the same for both races. The one exception was nonmalignant respiratory disease where the whites had a ratio of 3.70 and 4.41 in the two age groups respectively, and the nonwhites had ratios of 3.92 and 3.10. Although comparisons by sex were not possible for the steelworker study, this factor has been shown to have an effect on the ratio. Olsen et aE. [Z] have slightly, but consistently higher ratios for women than for men in the category of all causes for all age groups. Although many factors may affect the value of the ratio, the ratios for different groups are similar for most diseases, even when compared across studies. Table 4 shows the values of the ratio for selected causes as reported by Guralnick [l] for a one third sample of deaths occurring in the U.S.A. during 1955, and by Olsen et al. [2] for a 50% sample in the State of California during the same year. Also included in the table are the ratios for the entire steelworker cohort. Although no adjustments have been made for sex, age and race, the results from these three studies are quite similar. The lower value for the steelworkers in “all causes”, and several of the cardiovascular categories may be indicative of the fact TABLE
4.
COMPARISON
OF
ASSOCIATED
SELECTED
CAUSES
TO
PRIMARY
AMONG
THREE
CAUSES
OF
DEATH
Steelworkel study*
GLWhXk’S
Cause
of death
study
R&ratory cancer (162-163) Diabetes melhtus (260) Vascular lessons of CNS (330-334) Arteriosclermtlc and degenzratwe heart disease (42W422) Hypertensive dwase W&447) General artermsclerosls (450) Nonmahgnant resputory disease (478-527) Curhow of the hver (581) Accidents (80&962)
Causes NA
of death are coded according available. white and non-white.
= Not
*Both
to
RATIOS
STUDIES
I91 105 104 246 1 75
2 05 1 08 104 3 18 1 95
1 58 I 08 104 261 151
132 2 50 7 50
1 34 2 69 NA
I IS 7 84 1507
3 79 I51 1 22
4 46 NA 118
321 143 108
the 7th Revision
of ICDA.
FOR
Evaluation of Multiple Causes of Death
189
that it is a younger population. The most striking difference is for general arteriosclerosis (450). The ratio for the steelworkers study is nearly twice as large as the one reported by Guralnick. The discrepancy is mostly due to a coding difference. In the steelworkers study, whenever 422.1 appears as the primary, 450 is automatically coded as a contributory cause of death. If the investigator finds it necessary to make comparisons among different groups where the coding rules used by the nosologists were not the same, a detailed examination of these ratios may be useful. In fact, any comparisons on primary causes between two groups when the ratios of associated to primary causes differ, should be examined closeiy. If the difference in the ratios is due to a factor for which an adjustment has been made in the comparison of primary causes (age, sex, etc), then the discrepancy may be due to the effect of these factors on the unadjusted ratio. However, if the difference in the ratio is due to a failure of a condition to be reported as a primary cause, misleading interpretations may result. One example of what may occur is illustrated by digestive cancers among the steelworkers in the mason department [12]. For the white mason workers, the expected number of digestive cancer was 9.0 compared to an observed of 3.0, with a relative risk (the MantelHaenszel summary Chi-square procedure [13]) of 0.33 (11< 0.10). Because of the small numbers, and the borderline significance, no strong statements could be made and the results were considered only suggestive. However, the suggestive nature of the data becomes weaker when one notes the ratio of 1.67 for stomach cancer in the white mason workers. Two additional death certificates mentioned stomach cancer, but the latter was not coded as the primary cause. For general populations or all steelworkers, less than 5:/d of digestive cancers appear as a contributory cause, but for the mason workers, there were 40?.. However. one must interpret this result with caution, since the numbers are fairly small. One possible way in which multiple causes of death might be used is to tabulate the contributory causes in the same way that the primary causes have been used. Thus, we evaluate mortality using the number of certificates that have a particular cause mentioned, either as a primary or contributory cause. As mentioned earlier, the term ‘associated cause of death’ has been suggested as a measure of prevalence at the time of death, and has already been used to evaluate the steelworker data [lo]. It is interesting to compare the results of the analysis using only the primary cause with those using associated cause of death. A comparison was made of the causes of death which were indicated as having high relative risks (p < 0.05) by either method in one of the 57 work areas defined in the study. For the purpose of this study, the first job in 1953 determines one’s work area, and all other areas are used as the control [14]. Evaluations were based on the 27 causes of death outlined in the report [lo] in addition to diabetes (260.0) and hypertensive heart disease (44&447). Comparisons were made for all possible combinations generated by race, work area and cause of death. Fifty-four of these race-area-cause combinations were identified to have a significantly high relative risk by either or both approaches. Of these, 27 were found to be significantly high using both methods of analysis. In this report, we would be more interested in those identified by only one method. Table 5 shows the observed and expected deaths, and relative risks using both methods of analysis for the work areas and causes of death for which the relative risk based on primary cause was significantly high, but the relative risk based on associated cause was not. Of the 9 cases where this occurred, 2 were in the categories of cerebral vascular lesions and hypertensive disease. Two of the discrepancies were in remainder categories (‘all other cardiovascular renal disease’ and ‘all other causes’). Significance in these nonspecific categories is always difficult to interpret and since for these two work areas a further subdivision did not show more specific findings, no further commem will be made. The loss of significance for all cancers among the white ‘general technical’ workers is not the result of a large change in relative risk, but rather a small shift across the
OTTO WONG et al
190 TABLE 5. CAUSESOF DEATH BY WORK CAUSE WAS SIGNIFICANTLY HIGH
Work
area
G.Xler.Xl techmcal Coke plant Machme shop Weld shop Sheet fimshmg shlppmg El.XtrlC furnace Billet. bloom & slab mills Anneahng. normallzlng
Continuous ptcklmg electrIca
Race
Cause
of death*
AREA AND
AND
RACE
ANALYSIS
FOR WHICH
BASED
ON
ANALYSIS BASEDON
ASSOCIATED
CAUSE
PRIMARY
WAS
NOT
Obs
White White White Whlre
(14&?05) (177-181) (33&334) (330-334)
62 9 31 I1
46 8 3 84 19 I 53
White
(444-468, (592-594)
14
Wh,te
(44&4J7)
White
(44&G&7)
21
White
(47&527)
14
White
all other causes 1
5
5
I 2 I ?
35: X4$ 688 1st:
62 9 38 II
49 7 50 28 7 78
I27 202 I 35 43
72
2 35b
52
41 6
I 32
14
4 67b
5
J6
I 10
II 4
2 osa
42
34 b
125
74
2 ClSt.
34
27
I 30
13
-I 31:
I
and
cleanmg
* ICDA, 7th Revision. t Significance of relative risk based on Chl-square with $ Significant at 5’6 level. $ Significant at 14; level. // Causes excludmg respiratory tuberculosis (OOl&OOS), all renal disease (330-334, 400468. 592-294). non-malignant rhosis of liver (580), accidents (800-962). horrucides and ill-defined causes (780-795).
6
one degree
26
244
of freedom.
cancers {14&205), respiratory &ease suicides (963-964,
cardiovascular(470-537). clr97%985), and
arbitrary p = 0.05 boundary. Several of these mmor changes will occur whenever a comparison of this type is made. The annealing normalizing work area showed a change in significance for the category nonmalignant respiratory diseases, but more detailed subdivision within the category was not possible because of the small frequencies. The final discrepancy is of particular interest since it concerns one of the more important findings in regard to previous analysis of the steelworkers cohort. The high kidney cancer in coke oven workers has already been noted [lSj. In this case, the loss of significance when prevalence as indicated by associated cause was evaluated is due to the disproportionately high number of kidney cancers within the urino-genitary cancer category, as compared to the entire steelworker cohort as a whole. Table 6 shows the 17 cases for which the relative risk based on prevalence was significant, but the relative risk using primary cause faiied to be significant. From these 17 cases, 6 were ones for which there were less than 5 observed deaths when analysis was based on primary causes and thus no relative risk was computed. Four of the cases are in nonspecific remainder categories. Five others show only a slight shift in the relative risk and the resulting significance is due mainly to the increased sample size when prevalence is used in the evaluation. The major finding is the lung cancer in the white machine shop workers. This was suggestive when using analysis based on primary cause, but with analysis based on prevalence this could have been anticipated it became significant at p < 0.01. However, because
of the
for
department
this
higher
ratio
of
associated
to
primary
causes
of death
in lung
cancer
(26/21 = 1.24). DISCUSSION
Some of the problems encountered in using multiple causes of death data in occupational mortality studies have been discussed. Some suggestions have been made and in some cases evaluated using a population of steelworkers. The relevance of using this type of data to evaluate mortality patterns in occupational cohorts has been emphasized. The study of individual disease combinations is the most general approach to the analysis of ‘multiple cause’ data, but unfortunately the number of possibilities
Evaluation
6. CAUSES OF DEATH
TABLE
my WORK
SIGNIFICANTLY
HIGH
of Multiple
AREA AND AND
Causes
RACE FOR
ANALYSIS
of Death
ANALYSIS BASED ON
WHICH
BASED
191
ON
PRIMARY
CAUSE
WAS
ASSOCIATED
Transportatmn and yard Black\mlth shop MachIne shoe Macon dept Machmc shop Gellerdl techmwl StaInlea ulnealmg pxhhng and procesr1ng Heat treatme and forge Janitors
Jamtors Plant protectlo” Sheet fimshmg and shlppmg General techmcal Pipe shop Plant protectmn Ran1 mill fimshiog Annealmg normallzlng Jamtors
* i 1 $ ~
area
Cause
of death’
Wh,te
1150 ISYI
White White White Wh,tu
(16&1641 IlhO-1641 ilb?-163, (I62-1631 (165. 170 192-1991
Wh,te
(165. 170 192. 1991 (33lK334) (400 4561 (592 -594) (33&3341 (40s 456) 1592-5941 i42&42?1 (4l%419, (423-443,
White
NonwhIte White
White White
(4O&ll9) (423 443) (444-468) (592-5941 (44&4681 1592-5941 i44&46Xi (593-5941
Nonwhlie White Whnte
White NonwhIte White White
ICDA 7th Revision. Slgmficance of Relative Significant at 5”/, level. Significant at 1’; level. Less than 5 deaths.
Risk
based
Ohs
EXP
Rlakt
Obs
E~P
Rel
Rlskt
I39
I 55
153
1 61:
21 13 IX
32 I42 73 135
2 13 I51 Xl
I I 3h
34 150 77 141
242t I 79b 86: I 68$
I IX
25 5
I 441
I I
5 376
51
01
I
14
I6 83
I 76
6 20
19 II8
3 70+ I 791
32
32 I
I 37
50
366
I 45:
IO
5x
I 76
28
186
I 54:
0
04
I6
6 42~
69 31
IO1 64
I
55 18
37 3 19 1
I 539 I 50$
41
‘01
35
24 7
146:
2x
3 34:
10 63
3 62: 202
1470.527)
I6
(001-008) (44&447)
11 1’)
on Chl-square
Rel
21
6
with
one degree
WAS
Assoaated
Pnmary Work
CAUSE
NOT
6 I?
of freedom.
results in extremely fractionated data. A compromise between the desire to incorporate contributory causes in the analysis and to keep the total number of classifications manageable is to use a cross-tabulation of primary by contributory causes. Analysis can be based on the individual cells of the matrix in a similar manner to the usual evaluation of primary cause only. Although analysis based on the individual cells may provide useful information in regard to disease combinations, several problems are encountered. First, similar cross-tabulations for the total U.S. and other standard control groups have not, in general, been produced, one exception being for the year 1955 [ll]. Thus, unless an internal comparison is being made, there is no way to obtain an expected number for some of the cells. Second, if further subdivisions are made on the basis of age, sex and race, the numbers for many disease combinations may be too small for reliable statistical interpretation. In spite of these problems, the matrix is beneficial to tabulate. First, it provides an additional method of editing the data. Thus, improbable combinations can be rechecked for possible abstracting or coding errors. In addition this type of tabulation gives some insight into which diseases within the cohort are associated with one another. This type of information may be useful in determining which disease combinations should be tabulated for standard populations. Finally, in the case of more frequently occurring combinations, internal comparisons or comparisons with other study groups could be made, thus refining the analysis presently used to study differences in mortality patterns of two working groups. An additional advantage of this type of tabulation is that it incorporates many previously used statistics as special cases of this generalized concept. The frequency
192
OTTO WONG et al.
of the primary cause of death, the ratio of associated to primary frequencies for a particular cause and the 2 x 2 contingency tables suggested by Guralnick are all definable in terms of this matrix. Additional examples are readily available. The ratio of the diagonal element to the difference of the column total and diagonal element has been tabulated by others [l, 2-J. A ranking of these proportions will indicate which contributory causes are most often associated with particular primary causes. For example, among the 252 deaths with nonmalignant respiratory disease listed as the primary cause, (505-252) or 253 contributory causes were coded. Arteriosclerotic heart disease (47/253) and car pulmonale (47/253) are most often coded among these contributory causes. Another widely used statistic is the PMR (Proportional Mortality Ratio). The ratio of the diagonal entry to the sum of the diagonal elements is equivalent to a proportional mortality ratio where no adjustment has been made for age. This ratio can be age-adjusted by using similar ratios from some standard populations. An investigation was made to evaluate the extent of usefulness of two of the statistics employing multiple causes of death data. First, the ratio of associated to primary causes of death was evaluated in detail. Although the ratio does vary for different age, race and sex groups, it is similar for many diseases even between various studies. One possible use might be to compare the ratios before contrasting mortality patterns of sets of data where the rules used by the nosologists may differ. Likewise, it may be used to screen for situations where comparisons based on the primary cause would be misleading. This is particularly useful in those cases where there are not many observed deaths, thus a larger than expected number coded as a contributory rather than a primary cause could easily affect the result. Again it is emphasized that a ratio different from one does not necessarily indicate an elevation or a deficit in mortality risk, but a difference in the ratios between two groups being compared should deserve further attention. A second statistic evaluated in detail is the ‘prevalence’ of the disease of the associated cause of death on the death certificate. Comparisons of results of analysis based on this statistic with those based on primary cause of death indicated that the gain in information is not as large as one might anticipate. In fact the increase in sensitivity appears to be due mostly to an increase in sample size thus resulting in an increased number of statistically significant results. In addition, discrepancies in the two methods seemed to occur mostly for cerebral vascular lesions, hypertensive disease or nonspecific remainder categories. Although the multiple cause procedures presented here are useful in describing contributory causes, and in a few instances have resulted in improved interpretation of the data, the increased amount of information obtained when comparing an occupational cohort with another population has not been large. This suggests that the additional effort necessary to incorporate multiple causes of death data into a study of this type may not be necessary. Three qualifications to this statement must be reiterated. First, this does not apply to the use of multiple causes of death data in situations other than those described here. Second, evaluations were based on the steelworker population. Although the gain in information using multiple cause data was slight for this group, this may not be true for other populations. Finally, the more complicated problem of using certain disease combinations has been suggested, and, if frequencies permit, should be dealt with more completely in the future.
SUMMARY
(1) The formulation of a matrix of primary by contributory causes serves to give an overall picture of the mortality pattern of an occupational group and has the additional advantage of being useful as an editing device. If tabulations also exist for an appropriate control, comparisons may be made for disease com-
Evaluation
of Multiple
Causes
of Death
193
binations of interest. If frequencies are large enough, adjustments can readily be made for age, sex and race. (2) A comparison of the proportion of deaths from a particular primary cause with a particular contributory cause mentioned on the death certificate can be obtained. This is a refinement of the current practice in comparing mortality patterns. (3) The ratio of associated to primary causes of death may be used to determine if results based on the primary cause are misleading. It may also be used prior to comparison as a screening device to determine if the coding schemes between two different studies are comparable. In general, however, this ratio is not very sensitive to the type of populations being compared. (4) For the steelworker population, analysis based on associated cause yields only a slight gain in information as compared to that based on the primary cause alone. Although it is clear that multiple causes of death coding is necessary in certain applications, it is not clear that the gain in information is worth the effort of routinely coding and analyzing multiple causes of death in every study. Two possible exceptions are when the disease is usually only present as a contributory cause (for example, car pulmonale) or when special disease combinations are of interest. REFERENCES
t 2. 3. 4. 5. 6. 7. 8. 9 10. il. 12. 13. 14 15.
Guralnick L: Some problems in the use of multiple causes of death. J Chron Dis 19: 979-990, 1966 Olson FE, Norris FD, Hammes LM, Shipley PW: A study of multiple causes of death in Californib. J Chron Dis 15: 157-170, 1962 Dorn HF, Moriyama IM: Use and significance of multiple cause tabulations for mortality studies. J Am PuW Hlth Ass 54: 40%406, 1964 Markush RE, Siegal DG: Prevalence at death-l. A new method for deriving death rates for specific disease. J Am Pub1 Hlth Ass 58: 544-557, 1968 Krueger DE: New numerators for old denommators-Multiple causes of death. Nat Cancer Inst Monograph 19: 431-3, 1966 Cohen J. Steinitz R: Underlying and contributory causes of death of adult males m two districts. J Chron Dis 22: 17-24, 1969 Lloyd JW, Ciocco A: Long-term mortality study of steelworkers-I Methodology. J Occup Med 11: 299-310, 1969 Redmond CK, Snuth EM, Lloyd JW, Rush HW: Long-term mortality study of steelworkers-3. Follow-up. J Occup Med 11: 513-521, 1969 Manual of the International Statistical Classification of Disease: Injuries and cause of death WHO. 7th Revision, Geneva, 1957 Redmond CK: Comparative cause-specific mortality patterns by work area withm the steel mdustry. HEW Publ. No. (NIOSH) 75-157, 1975 National Center for Health Statistics: Vital statrstlcs instruction manual-2. Cause-of-death coding. 1955 Rockette HE, Redmond CK: Long-term mortality study of steel-workers-10. Mortality patterns among masons. J Occup Med 18: 541-545. August 1976 Mantel N, Haenszel W: Statistical aspects of the analysis of data from retrospective studies of disease. J natn Cancer Inst 32: 719-248. 1959 Lloyd JW. Lundin FE, Redmond CK. Geiser PB: Long-term mortahty study of steelworkers-4. Mortality by work area. J Occup Med 12: 151-157. 1970 Redmond CK, CIOCCO A, Lloyd JW, Rush HW: Long-term mortality study of steelworkers4. Mortality from malignant neoplasms among coke plant workers. J Occup Med 14: 621-629. 1972