American Journal of Industrial Medicine 20:285-306 (1991)

RESEARCH ARTICLES

Mortality of Employed Men and Women Peter Goldblatt, MSc, PhD, John Fox, DIC, PhD, and David Leon, BA

This paper presents mortality data for a 1% sample of men and women in England and Wales who were employed at the time of the 1971 Census of Population. It provides background information on the “healthy worker effect” by age, social class (as determined by occupation), cause of death, and length of follow-up. As expected, relative mortality of those employed at census rose with subsequent follow-up. This effect was strongly age-related, apparently as a consequence of the development (and increasing predominance) of chronic diseases with age. This suggests a unified explanation for some of the variation described in the literature. Statistical modelling of the relationship between mortality and length of follow-up confirmed that the healthy worker effect did not entirely disappear as follow-up progressed in this study. We examined social background as an explanation for this persistently low mortality, but found no evidence to suggest that it was an important factor. Key words: healthy worker effect, prospective mortality follow-up, age effect, lifestyle, length of follow-up, cause of death, statistical models

INTRODUCTION

The use of death rates from a reference population to study the relative mortality levels of workers in specific industries or occupations has a long history [Registrar General, 18551. The most widely used standard values are the age- and sex-specific rates for the area or country in which the workers reside. This is true both of cross-sectional analyses of routine statistics [Registrar General, 1855, 18751 and of follow-up studies of industrial cohorts [Doll, 1952; Enterline, 19641. Despite the ubiquity of this practice, the appropriateness of general population rates to individuals in every type of work has generated considerable critical debate. In the context of cross-sectional comparisons, Ogle pointed to two factors which could obscure the hazards associated with specific occupations [Registrar General, 18851. These may be paraphrased as follows: Social Statistics Research Unit, City University, Northampton Square, London, England (P.G.). Office of Population Censuses and Surveys, St Catherine’s House, 10 Kingsway, London, England (J.F.). Department of Epidemiology and Population Sciences, London School of Hygiene and Tropical Medicine, London, England (D.L.). Address reprint requests to John Fox, Medical Statistics Division, Office of Population Censuses and Surveys, St Catherine’s House, 10 Kingsway, London WC2B 6JP, England. Accepted for publication November 28, 1990.

0 1991 Wiley-Liss, Inc.

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(i) differences between jobs in the health status required of workers at entry (with some jobs limited to the strongest and healthiest, while others attract a disproportionate number of those with existing health problems); (ii) variations in the level of health required to remain in a particular job (with workers whose health fails them either moving to less demanding jobs or else moving out of the labor force entirely). When either of these applies, comparison between mortality levels of affected occupations and those of the general population can lead to incorrect inferences concerning the hazards associated with these occupations. Instead of occupation determining health status, causality operates in the opposite direction. As several authors have indicated [OPCS, 1978; Weed, 1986; Ostlin, 19891this phenomenon, of “health-related selection” [Fox and Collier, 1976; Fox et al., 19821, was recognized by many of the early writers on occupational mortality [Ramazzini, 1713; Guy, 1845; Farr, 18851. A separate problem concerns the source of hazards experienced by a workforce. Are the shared hazards specific to the work or workplace, or do they relate wholly or in part to a common life-style or environment [Registrar General, 1923; Fox and Adelstein, 1978]? In the latter case, comparison with a general population with a wider social background is again misleading. The existence of such a problem was recognized by Farr [Registrar General, 1855, 18751, although it was Stevenson who sought to quantify its effect using the newly derived social class schema [Stevenson, 1923; Registrar General, 19271. These social and geographic components of occupational mortality he described as the “effects of way of life” [Registrar General, 19231 and they correspond loosely to what are now called “life-style factors.” Considerable attention has been given to these influences in recent decennial analyses of cross-sectional data [OPCS, 1978, 19861. While both health-related selection and life-style are recognized as important factors which can distort the results of cross-sectional analyses [Bertillon, 1913; Yule, 1934; OPCS, 1978, 19861, it was the proliferation of follow-up studies of occupational cohorts in the 1970s that focused attention on the question of selecting appropriate reference rates [Fox and Adelstein, 19781. The occurrence of low mortality in a number of cohorts was first noted by Goldsmith [ 19751. McMichael [ 19761 coined the term “healthy worker effect” for this phenomenon: Enterline [1975] and Gaffrey [1975] pointed that it was cause-specific; and Fox and Collier [1976] linked it to health-related selection, as described by Ogle. Under the latter model, the effect is time dependent when observed in a followup study, with mortality lowest at the start of follow-up. This initial mortality deficit is greatest if follow-up commences at recruitment, as this represents a direct measure of the impact of selective entry into employment on subsequent mortality. As time passes, chronic illnesses develop in previously healthy workers and their initial relative health advantage dissipates. Conversely, many of those who were precluded from employment by illness at the commencement of follow-up (and hence excluded from study) have either recovered or died when comparisons are made at later points in time. Differences in health status between the study population and others thus tend to decline with time. A similar pattern is predicted by this model for studies of current workers for

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whom follow-up commences on a date unrelated to starting employment (for example, from a fixed point in time or the date of responding to a survey). In this situation, not only will there be a residual effect of selection at entry, which will vary according to length of employment, but the fact of remaining in employment would be indicative of selective survival. Again, this relative advantage will diminish with increased follow-up (although the magnitude of health-related selection will tend to be less in the latter type of study than in the former). Several authors have pointed out that healtk-related selection is not the only possible source of artifactually low mortality in an occupational cohort. Under-recording of deaths occurs in some studies [Shindell et al., 1978; Doll, 19841 either because early deaths are excluded from the study population or because follow-up is imperfect. Others have pointed to differences in the life-styles of those in and out of employment [Wen et al., 1983; Carpenter, 1987; Wilcosky and Wing, 19871, with health consequences which persist for many years [Bertillon, 1913; Moser et al., 1987; Fox and Shewry, 1988; Goldblatt, 19901. The observation of low mortality in an employed cohort may thus reflect a combination of several factors, each of which may be assumed to vary in its effect by age [Fox and Collier, 1976; Wen et al. , 19831, as well as by cause of death. These issues are addressed in this paper.

MATERIALS Data were obtained from the Office of Population Censuses and Surveys’ (OPCS) Longitudinal Study (LS) of England and Wales [OPCS, 1973; Fox and Goldblatt, 1982; Goldblatt, 19901. In the LS, routinely collected OPCS records are brought together continuously over time for a 1% sample of individuals resident in England and Wales (approximately half a million people alive at any one time), starting with persons enumerated in the 1971 Census of Population [OPCS, 19731. The most convenient way of achieving this sampling fraction, which at the same time facilitated linkage of records, was by including people born on 4 particular days of the year. Fuller details of the method of selecting the sample and of prospective vital event recording in the study are described in published reports on mortality data from the study [Fox and Goldblatt, 1982; Goldblatt, 19901. One aim, in obtaining data on employment and mortality from the LS, is to meet a need expressed by a number of epidemiologists [Gardner, 19841 to have nationally representative data on the magnitude of the “healthy worker effect” by sex, age, cause of death, and length of follow-up to facilitate correction of general population reference rates. Summary figures for the early period of follow-up of the sample (1971-75) have been published [Fox, 1979; Fox and Goldblatt, 19821 and more extensive data are now available [Goldblatt, 19901. For this reason, only a brief summary of the employed population is given here. By exploiting the size and richness of the data set, we take the opportunity to investigate how the healthy worker effect is related to age and length of follow-up, both by using statistical modelling techniques and by contrasting the influences of age at entry and of that at death. We also use social class to investigate possible effects of pre-existing behavioral and life-chance factors on the long-term effects of employment on mortality [Registrar General, 1923; Carpenter, 19871. The population studied in this paper was drawn from the 513,071 people in the

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initial sample who were traced in the National Health Service Central Register (NHSCR) by 1977. Analysis of their mortality is based on events which occurred between the 1971 and 1981 censuses. Employed men and women were identified from the 1971 census question concerning “job last week” (which only those aged 15 and over were required to answer). They included anyone (187,933 men and 146,121 women in the study population) who ticked the statement: “YES - in a job at some time during the week.” Instructions on the schedule indicated that this was intended to cover all those with a job “even if it was only part-time or if the person was temporarily away from work, on holiday, sick, on strike, or laid off.” Conversely, everyone else was instructed to give a negative reply to the question; the alternative categories suggested were “seeking work or waiting to take up a job,” “intending to seek work but sick,” “wholly retired,” “housewife,” “student,” or “permanently sick.” Information collected on the occupation of those in employment related to their main job in the week preceding the census. For men out of employment, information was collected on last occupation. In this way, the social class of men, as defined by their occupation, can be identified and is used in this paper to compare those in and out of employment. As housewives were not required to provide information on their last occupation [Goldblatt, 19901, this analysis cannot be carried out for women. As an alternative, in comparing women in and out of employment, account is taken of their marital status at census and, if married, their husband’s social class [Moser et al., 1988; Goldblatt, 19901.

METHODS The analyses presented in this paper are based on person-years-at-risk [Clayton, 1982; Berry, 19831 calculated, for each sex, by age and time period using techniques developed for the LS [Goldblatt and Fox, 1977; Fox and Goldblatt, 19821. Individuals were considered to have entered the study on Census Day 1971, and all death rates are then calculated by dividing the number of deaths occurring in each age group and time period by the corresponding person-years at risk. Here, two different methods of tabulating person-years-at-risk and observed deaths are used. First, by age at risk of death and calendar period, and, second, by age at census and time since census [Goldblatt, 19901. In both cases, age is tabulated in 5-year age groups below age 85 and in a single age-band at older ages. Comparisons are made between mortality rates of the employed and those of the entire LS sample for each cause of interest [Goldblatt, 19901, based on underlying cause of death as determined by OPCS according to the 8th revision of the International Classification of Diseases [WHO, 19691. These comparisons are presented either as age- and sex-specific rate ratios (expressed in percentage terms) or as standardized mortality ratios (SMRs). The latter are the percentage ratio of observed to expected numbers of deaths among the employed, with expected numbers obtained by multiplying age, sex, and time period specific person-years-at-risk for the employed by the corresponding death rates for the entire LS sample [Fox and Goldblatt, 1982; Goldblatt, 19901. Measurement of the statistical variability of each SMR is expressed as a confidence interval (CI). These are calculated from the range of expected values con-

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sistent, at the 95% probability level, with the number of deaths observed [Breslow and Day, 1985; Goldblatt, 19901. In deriving this range, it is assumed that, for any given expected value, the observed number follows a Poisson distribution with a mean equal to this expected value [Breslow and Day, 19851. Relationships between SMRs are characterized using log-linear modelling of observed and expected deaths [Breslow et al., 19831. As this corresponds to a standard generalized linear model [McCullagh and Nelder, 19831 with a Poisson error structure, estimation procedures, based on maximum likelihood methods, are provided by the computer package GLIM [Baker and Nelder, 19781. In analyzing mortality of employed men and women in the LS, it is necessary to enter two caveats. First, death rates presented here inevitably reflect the specific design of the LS. They therefore incorporate the combined effects of the small degree of overall under-ascertainment of deaths in this study, of any under-recording peculiar to the employed, and of health-related selection. While under-ascertainment of deaths occurs in all studies, its magnitude is highly sensitive to study design. For this reason, although the Longitudinal Study appears relatively free of such problems [Goldblatt, 19901, here more emphasis is given to relative differences in mortality. These relative values can be used to adjust any standard rates so as to correct for studying employees in particular. Other reasons for under-ascertainment of deaths require study design to be taken into account. The second caveat is that the employed population described here was followed from a single date unrelated to their individual work histories. No information was collected on date of starting (or terminating) work. Thus, the extent of health-related selection reported here is, in particular, likely to be less than that for a comparable cohort followed from entry into employment (although the difference, in this instance, will mainly be confined to the period shortly after follow-up commences).

RESULTS Employment Patterns by Age and Sex

Figure 1 provides an indication of the variation by age in the percentage of the sample who were identified as employed in 1971. Over 90% of men were in employment between ages 25 and 59; the largest other single category was the 3% seeking work. Below age 25,73% were in employment, 21% were students, and 5% were seeking work. Above age 59, the percentage in employment declined markedly with increasing age. Although this reflected retirement at age 65 and over, 22% were out of employment at ages 60-64, principally comprising 5% seeking work, 6% permanently sick, and 7% retired. The pattern of employment amongst women was completely different. Not only were a smaller proportion of women than men employed at every age but the proportion in employment at ages 25-34 (42%) was lower than at other working ages (55% for each of the age groups 15-24 and 35-59). This pattern reflected the domestic responsibilities of women, with most of those who were not in employment at these ages recorded as housewives. At older ages, most women were recorded either as housewives (39%) or as retired (48%). A more detailed discussion of women’s employment characteristics in this sample can be found elsewhere [Goldblatt, 19901.

290

Goldblatt et al. Males I00 Y0

xo 7(I 60 L

C

;50 a" 40

30 70 I(l (1

Other inactive

I

704

Age

Fig. 1. Distribution of males and females in England and Wales by economic position and age, 1971.

All Causes of Mortality by Age at Death Age- and sex-specific death rates for those employed at census are shown in Table I. Rates for the early part of follow-up (1971-75) are shown separately from those for later years (1976-81). Ratios of these rates to those of all men or women in the Longtiduinal Study are also presented in this table. Since these ratios are less

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TABLE I. Death Rates in 1971-81 for Men and Women in England and Wales Employed in 1971 bv Calendar Period and Age at Death Men

Women Age specific ratiosb

Age specific ratiosb

Age at death

71-75

76-81

71-75

76-81

71-75

76-81

71-75

76-81

20-24 25 -29 30-34 35-39 40 -44 45-49 50-54 55-59 60-64 65 -69 70-74 75-79 80-84

622 704 767 I127 2259 4500 7359 13040 19618 32169 43100 63494 114435

643 692 902 1394 2368 4336 7995 13188 22493 35093 53235 84062 115930

87 86 87 82 89 88 87 85 83 77 67 68 79

60 86 89 89 91 97 93 95 94 90 85 85 83

502 512 568 773 1986 2326 4374 6349 9535 10592 21677 24395 35854

245 330 733 766 1339 271 1 4326 6366 10824 15000 24468 42022 80880

104 85 65 73 101 74 79 79 71 52 62 42 38

58 87 106 98 105 91 87 84 88 81 78 75 87

Death ratesa

Death ratesa

~~

“Rate per million person-years-at-risk. bRatio of the age specific rate for the employed to that for all men or women in the LS.

affected by factors such as under-reporting of mortality in the study as a whole, they provide relative reference values for an employed population which have a wider applicability to other cohorts of employees than do the absolute rates. It is clear from this table that relative mortality levels for the employed increased with length of follow-up at most ages. At ages 45-64, mortality of employed men rose to a level approximately 5% below that of all men in the period 1976-8 1. At other ages, it rose but was still 10 to 15% lower in this period. Among women, rates for the employed in 1976-81 did not differ consistently from those of other women at ages 30-49, but were generally 15 to 20% lower at other ages. The general tendency for SMRs of the employed to increase with follow-up is illustrated, on an annual basis, in Figure 2. For each broad age group at death, it is evident that there was a marked year-by-year increase in rates, relative to the whole population, between the beginning and end of follow-up. Comparison of Mortality by Age at Entry and Age at Death

Among the employed, differences in the size of mortality deficits by age were apparent at entry to follow-up and persisted in subsequent years (Fig. 2). Since people age with length of follow-up, tabulations by age at death cannot, however, be used to identify the independent effects of age and length of follow-up on mortality rates of the employed [Osmond and Gardner, 19821. The problem can be illustrated using data from Figure 2. Men aged 55-64, for example, had an SMR of 72 shortly after entering the study in 1971 and those aged 65-74 an SMR of 57. In 1980, the last full calendar year of follow-up, the respective figures for these two age groups at death were 97 and 92. As this was almost 10 years later, those dying in 1980 at ages 65-74 largely comprised men aged 55-64 at entry. Thus, SMRs for the cohort aged 55-64

Goldblatt et al.

292

Age 55.64

1

Agc 65-74

50

71 73 15

77'79

71 73 75 7 7 ' 7 9

i 1 73Agc 75 7s+ 77 79

Fig. 2 . Mortality of employed men and women in England and Wales by age at death and year of death.

in 1971 had, in fact, only risen from 72 to 92, a lesser rate of increase than is shown in Figure 2 among men aged either 55-64 or 65-74 at death. Fortunately, the design of the LS provides a solution to this difficulty (without resorting to cohort approximations). As both the employed and those out of employment are followed-up in the study, rates for the employed and the reference group (all those in the study) can be tabulated by age at entry. Calculating relative mortality levels for each age cohort, on this basis, will then reflect changes over time more accurately. The relative mortality of employed men is presented both by age at entry and by age at death in Table 11. Two features of this comparison are worth highlighting. First, relative levels varied both more sharply and more consistently by age at entry than they did by age at death. For the former, they increased slightly between 15 and 34, were then constant to age 54, after which they decreased sharply with increasing age. This clear pattern is masked, when viewed by age at death, because each age group then contains a different mix of age-at-entry cohorts as follow-up progresses. The second important feature of this table is the difference in the numbers of deaths by age at entry and at death. Because of the young age structure of the employed, there were sufficient deaths (by age at entry but not by age at death) to analyze mortality of younger employed men separately. It is also evident that most of

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TABLE 11. Comparison Between Mortality in 1971-81 in England and Wales by Age at Entry to the Study and by Age at Death for Men Employed in 1971 Age group 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84

Mortality by age at entry

Mortality by age at death

Observed

Expected

SMR

Observed

Expected

SMR

81 107 137 190 355 633 1217 1755 2839 3231 1381 545 236 63

97.7 127.4 156.2 214.1 387.3 690.9 1213.6 1905.9 3147.4 3763.8 1716.5 765.1 314.9 82.0

83 84 88 89 92 92 93 92 90 86 80 71 75 77

20 55 I04 130 182 327 634 1129 1807 2568 2892 1793 767 292

21.5 74.5 121.2 147.2 21 1.9 362.6 688.8 1254.4 1999.0 2896.0 3422.2 2236.5 938.5 359.7

93 74 86 88 86 90 92 90 90 89 85 80 82 81

the deaths at ages 65 and over, among men who were employed at census, related to employment at working ages (and not to work beyond these ages). Consequently, when relative mortality levels of the employed are tabulated by age at death, they do not reflect the very low mortality of the select group who were working beyond normal retirement age. Does the Healthy Worker Effect Wear Off With Length of Follow-Up?

When these data are disaggregated by exact length of follow-up (Fig. 3), it is clear that relative levels of mortality depended both on age at entry and length of follow-up. They were consistently highest for those aged 35-54 and lowest for those aged 65 and over. After 6 months of follow-up, relative levels for each age group increased steadily with time. However, considerable random fluctuation was apparent in data disaggregated to this extent. For this reason, the most appropriate method of quantifying these rates of change and their relationship to age is by using statistical regression models. The data modelled comprised observed and expected deaths for 14 age groups at entry (5-year age groups from age 15 to 84) and for 12 follow-up periods (6 months, 1 year, individual full years up to 8 years, and then a single period covering the remaining 711 days). A family of log-linear models were fitted to these data, containing parameters which were intended to reflect the different ways in which initial effects could wear off with time, and to represent the ways in which this process might vary with age [Goldblatt, 19901. A single model was chosen from this family by including those terms which gave the largest (statistically significant) reductions in scaled deviance [McCullagh and Nelder, 1983; Goldblatt, 19901. Fuller technical details of this process are provided in the Appendix. The analysis of deviance for this model is shown in Table 111, with the formula provided by the model for estimating fitted values of SMRs by age at entry and length of follow-up. The model suggests that, based on data for 10 years of follow-up, SMRs for the employed ultimately “levelled-off’’ at a common value,

294

Goldblatt et al. Age 15-34

Age 55-64

Age 35-44

lfJ0F------

Year of folloa-up

Fig. 3. Mortality of employed men in England and Wales by age at census and length of follow-up

TABLE 111. Model Fitted to Mortality Date by Age at Entry and Length of Follow-Up for Men Employed in England and Wales in 1971* Effect of including terms in model in optimal order

Final parameter estimates

Term

Value of regression coefficient

Standard error

Deviance of model

Degrees of freedom of model

Mean

-0.08437

0.01097

180.2

167

-0.8726

0.1183

135.5

166

0.0549

0.0097 16

103.1

165

C3/t

(C/t)2

Reduction in deviance

Reduction in degrees of freedom

44.7

1

32.4

1

*In this table c represents age at entry in fractions of a century and t represents length of follow-up in years. From this model the SMR of those aged lOOc after t years of follow-up can be estimated by the formula: SMR = 91.9 X (0.4179)~c3~'~ X (1.0561)("~)~.

irrespective of age. However, the starting point for these SMRs and the timescale over which this occurred varied considerably by age (Fig. 4), as might be expected from Figure 3. Cause of Death

Variations in the magnitude of the healthy worker effect by cause are well documented [Enterline, 1975; Gaffrey, 1975; Fox and Collier, 19761. Table IV provides a summary of how SMRs at working ages vary by cause and length of follow-up for those in employment at census. For both men and women, mortality deficits at this age were greatest for respiratory diseases (SMRs of 66 and 70, respectively). Although based on relatively small numbers, both of these figures were significantly lower than those for all causes combined. The change with length of follow-up was also of a similarly large magnitude for both sexes (from 63 in 1971-75 to 77 in 1976-81 for men and from 58 to 74 for women).

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295

Age at entry

I 65

i

Broken line indicatesestimation beyond end of follow-up period

j

j

i s i i

9

ii)

1.1 1 2 1 3 1 4 1’s

Year of follow-up

Fig. 4. SMRs of employed men in England and Wales by age at entry and length of follow-up, estimated from optimal fitted model.

By contrast, SMRs for malignant neoplasms were markedly greater than for all causes combined (SMRs of 94 for men and 96 for women). Although there was an increase in mortality over time for both sexes from cancer, this change was of limited size (from an SMR of 91 to one of 97 for men and from 93 to 99 for women). This difference in cause-specific mortality patterns is consistent with an effect of healthrelated mobility on mortality since chronic illnesses (such as bronchitis) have a greater impact on the selective health status of current workers than diseases with short survival (such as lung cancer). While the data presented for natural causes in Table IV are all consistent with this model of health selection, mortality levels for accidents and violence shown in this table did not fit as well. Although these figures largely related to sudden deaths, SMRs in 1971-81 for this cause (90 for men and 85 for women) were comparable to those for natural causes. This suggests that the temporal effects of health selection may not explain all of what is called “the healthy worker effect.” Nonetheless, Table IV provides strong circumstantial evidence that it explained most of the effect in these data. Variation in the healthy worker effect by cause of death must, of course, play a part in explaining the influence of age at entry on mortality levels. Mortality of employed men by length of follow-up, age at entry, and cause of death are summarized in Table V. It is evident from this table that differences in age-specific death rates by cause resulted in changes in the distribution of deaths (both by age at entry and by cause) as ‘follow-upprogressed. Although circulatory diseases and cancer were the leading causes of death for each age group, the greatest contrast was between respiratory diseases and accidents and violence. Death rates for the former increased most rapidly with age, while those for the latter decreased over the working age range. Thus, among men aged 15-54, respiratory disease was a relatively insignificant cause of death throughout the 10 years, while accidents and violence made a

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TABLE IV. Mortality at Working Ages, by Calendar Period and Cause of Death, for Men and Women Employed in 1971, England and Wales Calendar period

1971-1 975 Cause of death

1976-1981

Observed Expected SMR

Observed ExDected SMR

(CI)”

(CI)”

Men aged 15-64 All causes Malignant neoplasms Lung cancer Circulatory diseases Ischemic heart disease Respiratory diseases Bronchitis, etc. Accidents and violence

3169 928 398 1565 1173 205 116 265

3721.7 1024.3 440.7 1796.4 1298.2 325.1 188.5 288.6

85 91 90 87 90 63 62 92

(82, 88) (85, 97) (82,100) (83, 92) (85, 96) (55, 72) (51, 74) (81,104)

3787 1163 427 1893 1460 22 1 114 244

4052.5 1172.2 492.0 1982.8 1504.1 285.1 144.2 277.6

93 97 96 95 97 77 79 88

(90, 96) (92,103) (87,105) (91,100) (92,102) (68, 88) (65, 95) (77,100)

Women aged 15-59 All causes Malignant neoplasms Lung cancer Breast cancer Circulatory diseases Ischemic heart disease Cerebrovascular disease Respiratory diseases Bronchitis, etc. Accidents and violence

719 356 61 96 I92 87 52 33 14 60

897.3 382.3 55.0 104.7 251.5 108.4 67.6 56.8 23.2 73.1

80 93 111 92 76 80 77 58 60 82

(74, 86) (84,103) (85,143) (74,112) (66, 88) (64, 99) (57,101) (40, 82) (33,101) (63,106)

772 403 57 121 204 108 58 42 26 49

878.8 406.3 56.5 124.5 258.4 124.5 73.1 56.5 27.6 54.9

88 99 101 97 79 87 79 74 94 89

(82, 94) (90,109) (76,131) (81,116) (68, 91) (71,105) (60,103) (54,100) (62,138) (66.1 18)

“95% confidence interval for SMR.

TABLE V. Mortality by Age at Entry, Length of Follow-Up and Cause of Death for Men Employed in 1971, England and Wales Cause of death Age at entry to

study

Length of followup

All

CaLISeS

Circulatory diseases

Malignant neoplasms

Respiratory diseases

Accidents and violence

ObExObExObExOhExObExserved pected SMR served pected SMR served pected SMR served pected SMR served pected SMR

15-54

0-5 years 1729 1962.4 88 5-lo years 2746 2929.7 94

462 823

496.4 839.1

93 98

821 936.6 1379 1397.4

88 99

88 135

144.7 1765

61 76

236 211

262.6 242.4

90 87

806 881.7 1102 1135.0

91 97

1290 1529.8 1831 2025.8

84 90

209 349

330.1 441.2

63 79

59 68

56.7 70.6

104

716.3 793.7

76 84

100

258.6 278.8

39 66

12

184

18.8 21.3

64 75

55-64 0-5years 2504 3001.0 5-lo years 3566 3910.2 65 and over 0-5 years 5-10 years

1013 1456.6 1237 1464.9

83 91 70

84

299 306

330.8 90 306.5 100

543 644

16

96

relatively major contribution at the start of follow-up but became progressively less important. For those aged 55-64, respiratory disease grew in importance as followup progressed, while the contribution of accidental deaths was consistently small. At older ages, this difference between causes was established from the start and subsequently changed very little. These changes provide one of the means by which cause-specific differences

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297

Fig. 5 . Mortality of employed men in England and Wales by age at census, length of follow-up and cause of death.

can affect overall mortality levels with continued follow-up. Figure 5 illustrates, in more detail, how levels for each cause varied with length of follow-up. For cancers, selection effects were of limited magnitude, disappeared fairly rapidly at each age, and were only slightly more marked as age at entry increased. The only difference between the patterns for circulatory diseases and for all causes was that health selection appeared to have a more marked effect at younger ages for the former cause group than for the latter. Initial mortality was low for respiratory diseases, particularly below age 55 and above age 64 at entry. Although levels rose with time, at no age had they risen to a figure much in excess of 80. In each instance there appears to be less variation with age at entry for specific groups of natural causes than for all causes combined.

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TABLE VI. Adjusting SMRs of Employed Men in 1971 for Their Social Class Distribution, Eneland and Wales

SMRs adjusted for social class

Unadjusted SMRs

Ail Men 1971-1975 1976-1 981 Social classes I-V only" 1971-1975 1976-1981 ~

Employed

All men

Employed

All men

85 93

I00 100

92 97

100 100

85 93

91 96

85 93

100 100

~

~

"In adjusting figures for social classes I-V only, account is taken of the exclusion of the unclassified.

The pattern of mortality from accidents and violence was somewhat different than that for natural causes. At younger ages, mortality levels decreased in the first year of follow-up and did not appear to increase consistently in subsequent years. At older ages, there were insufficient numbers to discern any pattern. The Effects of Social Class on the Mortality of the Employed

Work and way of life are so closely connected [Registrar General, 1923, 1927; Stevenson, 1923; Fox and Adelstein, 19781 that any examination of low mortality among employed groups must take their relationship into account [Carpenter, 1987; Wilcosky and Wing, 19871. Two components of this can be singled out in this study. The first concerns the extent to which the favorable social circumstances of the employed may have contributed to their persistently lower mortality throughout the study period. The second relates to the impact, on short-term trends in mortality, of variability in the magnitude of initial health selection among different occupational groups. A crude indicator of the effects of both these factors can be obtained by separating the employed according to their social class, as defined by occupation. The social composition of the employed population. In Table VI, the mortality of employed men is adjusted for their social class by using the SMRs of all men by social class. The permanently sick were not classified in a social class. By including the unclassified in the analysis, this method over-adjusts for any effect while it under-adjusts if they are excluded. To indicate the likely range of any social class effect, both sets of figures are included in the table. They show that social class adjustment accounted for little or none of the deficit; and, hence, low mortality among employed men was not a result of social class of origin. An analysis, analogous to that carried out for men, can be undertaken for employed women by adjusting their mortality for their marital status and, if married, their husband's social class. To exclude possible over-correction in this instance [Moser et al., 19881, a second set of figures are calculated for married women with a husband in social classes I-V (Table VII). In both instances, the deficit is not reduced, again suggesting that these aspects of a woman's circumstances do not contribute to persistent low mortality among those in employment. Variation in selection effects by social class. Differences in the way initial health selection effects wore off with time are illustrated (for men) in Table VIII. This contrasts SMRs at working ages in 1971-75 with those in 1976-81 for employed men

Mortality of Employed Men and Women

299

TABLE VII. Adjusting SMRs of Employed Women in England and Wales in 1971 for Their Marital Status and Husband's Social Class (if Married) SMRs adjusted for marital status and husband's social class (married women only)

Unadjusted SMRs Time period All women 1971-1975 1976-1981

Occupied

All women

Occupied

All women

86 91

100 100

84 90

100 100

88 94

77 88

100 100

Married women with husband in classes I-V" 1971-1975 77 89 1976-1 98 1

adjusting figures for married women with a husband in classes I-V, account is taken of the exclusion of unmarried women and of married women whose husbands were either unclassified or not included on their census schedule.

according to their social class. Increases over time were greatest among men in classes IV and V (those employed in unskilled or partly skilled manual jobs) and were not apparent for men in classes I and I1 (professional and managerial jobs). Table IX provides an indication of some of the comparable differences among employed women. For married women, there were few differences between those with a husband in classes I-V, but they all differed from the residual category. The latter included women not enumerated with their husbands, for some of whom this may have been a result of ill health.

DISCUSSION

The results presented in this paper have confirmed a number of previously reported features of the healthy worker effect. Principally, death rates in the LS among those employed at census were generally below those of other men and women, although this relative advantage dissipated with length of follow-up [Fox and Collier, 19761. Both the magnitude of the mortality deficit among the employed and the extent to which it changed with time varied by age, sex, and cause. Initially, mortality ratios were lowest amongst women and at older ages, but as their rate of increase varied, subsequent mortality deficits were least in middle age and continued to differ by sex. Mortality from respiratory diseases was initially lower than it was for other causes, but increased more rapidly with length of follow-up. The strong relationship between the effects of health selection and age were linked to cause of death in three ways. First, variation with age was less apparent for the two major cause groups (cancers and circulatory diseases) than for respiratory diseases and accidents and violence. Second, while the contribution of respiratory diseases to total mortality was greatest at older ages, this cause initially accounted for only a small proportion of deaths to the employed at any age. Third, there was a marked decline with age in the relative importance of accidental deaths (a cause which was not particularly affected by initial health status). The difficulties encountered when age at death is used to reflect changes in

1976-1981

45-64 1971-1975

1976-1981

All aged 15-64 1971-1975

1976-1981

65-74 1971-1975

1976-1981

75 and over 1971-1975

A.F. I.D.

IV V

HIM

I I1 IlIN

-

74 78 106 74 117 94 72 103 57 72 87 86 111 114 96 134

(17) (56) (52) (169) (81) (33) (10) (4)

75 75 85 87 85 99 53 130 69 75 105 94 I06 121 87 167 ___

126

64

75 76 88 86 88 99

67 75 103 93 106 121 91 162 -

60 67 76 73 82 69 388 131 -

60 77 79 89 98 103 151 -

~

94

80

64

67 75 82 56

77 61 83 81 93 96 179 91 -

Social (Ob(Ob(Ob(Ob(Ob(Ob(Ob(Ob(Ob(Obclass SMR served) SMR served) SMR served) SMR served) SMR served) SMR served) SMR served) SMR served) SMR served) SMR served)

1976-1981

15-44

1971-1975

Age at death

TABLE VIII. Mortality of Employed Males in England and Wales by Social Class in 1971, Period of Death and Age at Death

Mortality of Employed Men and Women

301

TABLE IX. Mortality at Ages 15-59 of Women in an Occupation in England and Wales by Marital Status, Husband’s Class (if Married) and Period of Death Husband’s social class

Period of death

1971-1975

1976-1981

Observed

Expected

SMR

Observed

Expected

SMR

25.6 129.4 80.4 262.2 127.9 42.4 53.0 720.9 242.4

47 66 70 85 81 80 126 81 103

17 99 66 268 126 30 51 647 200

28.9 136.6 81.6 274.0 122.0 37.9 47.8 728.8

59 72 81 98 103 79 107 89

Not married

12 86 56 224 103 34 67 582 250

All occupied women

832

963.3

86

847

197.0 925.8

102 91

Marital status Married

I I1 IIIN IIIM IV V Other All married

mortality over time were illustrated with data from Figure 2. A clearer picture of trends over time was obtained by analyzing the data according to the age at which men were employed (rather than at which they died) and then using statistical modelling techniques [Breslow et al., 19831to regress time trends directly on age at entry. However, by focusing on entry cohorts, it must be recognized that any differences between the age structures of the employed and the reference population within an age group will distort numbers of expected deaths. The severity of this problem is, however, generally of a lesser magnitude in this study (which uses an internal standard) than that experienced when reference rates are calculated on a cohort basis from external period rates. In assessing the findings of the regression analysis (Table IV and Fig. 4), a cautionary notes is required. A regression model is only reliable at or between fitted data points (in this instance ages 17.5 to 82.5 and follow-up between 3 months and 9 years), and its quality is, similarly, best for cells containing the largest numbers of expected deaths. Overall, the formula for fitted SMRs confirmed that the lowest ratios at each age occurred in the first few months of follow-up. Although the extent of this initial mortality deficit increased sharply with age (from age 17), the rate at which SMRs subsequently rose was also greater at older ages. There was some evidence that at younger ages, SMRs for the employed decrease in the early months of follow-up. However, there were too few deaths available for analysis to place much confidence on this finding. Two aspects of the fitted model warrant further caution. They are both a consequence of the uneven distribution of deaths by age at entry. First, the value which SMRs attain after levelling off might turn out to vary more with age when data on longer-term follow-up are available. It may be that the present model places too much weight on the values appropriate to ages 55-64 and that some variation with age is, in consequence, overlooked. Second, numbers of deaths at younger ages are relatively few and the model seems to be generally less accurate at these ages. Despite these caveats, the main conclusion of this analysis seems firmly based. For employed men of 35 and over, the relationship between mortality levels and

302

Goldblatt et al.

length of follow-up was consistent with a major effect of initial health selection. The size of this effect appears to have increased markedly with age, an observation which is consistent with the increase in chronic illness with age (a phenomenon reflected in census data, for example, by men who were recorded as out of employment due to “permanent sickness”). The time it took for this effect to wear off varied with age, despite the faster rate of increase at older ages. As Figure 4 illustrates, after 5 years there was little further variation with time for men aged 35 at census, but some changes were still occurring at the end of 10 years follow-up for those who were aged 60 and over at entry. It is apparent from this analysis that initial mortality ratios were least (and the subsequent increase over time greatest) in those age-sex groups which included the smallest proportion of people in employment. In part, this was simply a consequence of using ratios to represent differences in mortality levels [Wen et al., 19831. However, it could equally have reflected a real measure of the fitness of those able to work at points in their life when employment was less common, suggesting that the selectivity of employment (with respect to health status) varied according to its social context. Some authors have suggested that low mortality among the employed is associated with their advantageous social circumstances [Wen et al., 1983; Carpenter, 1987; Wilcosky and Wing, 19871. While it seems improbable that this could explain the short-term time trends in our data, it is more plausible as an explanation of the smaller, persistent deficit suggested by the model we fitted. Two components to this argument may be distinguished. The first relates to a greater propensity towards upward social mobility among the employed [Fox and Shewry, 1988; Goldblatt, 19881; this issue can only be tackled (within the LS) by using mortality data which extend beyond the 1981 census [Moser et al., 1987; Goldblatt, 19891. The second relates to the social origins of the employed. In this instance, a preliminary examination was possible by using data for the period covered by this paper to contrast the circumstances of those in and out of employment. In these data, the social differences which we quantified did not account for the persistence of low mortality among the employed. Similarly, changes in mortality levels of the employed with length of follow-up were not explained by social composition. However, it is important to recognize that the magnitude of selection effects can vary with social circumstances. The importance of age and sex have already been discussed. Equally important are the type of work performed and the conditions of employment (particularly those covering sick pay and the opportunities for returning to employment on recovery). These influence the likelihood of an individual remaining in employment despite serious or chronic illhealth. It was evident from our examination of social class variation among the employed that men in professional and managerial jobs were least affected by the healthy worker effect, while those in unskilled or semi-skilled jobs were most affected. It must be presumed that this reflected the specific phrasing of the census question [Goldblatt, 19901. Those who had a job in the previous week but were temporarily away from work sick were recorded as in employment; those who were out of a job in the previous week because of illness (even if they were intending to seek work subsequently) were not recorded as employed. This distinction can be assumed to have separated those sick men whose (higher status) jobs provided sickness benefits

Mortality of Employed Men and Women

303

from those in lower status jobs who were more likely to have registered for state benefits, so operating differentially according to job status. It is, however, unlikely that this accounted for all the differences between time trends shown in Table VIII. The likelihood of an individual being able (and willing) to retain his job despite ill-health is also important [White, 1983; Ostlin, 1988, 1989, 19901. The physical demands of the job, the economics of ceasing employment, the likelihood of re-employment after recovery, and the attitude of employers may all have combined to produce real differences by social class. In this context age is an important factor, for it is selection at older working ages that largely contributes to observed mortality differences. This is a reflection of the increased incidence of chronic illnesses (such as bronchitis) at these ages. These illnesses have an effect on overall employment levels through the numbers permanently sick or disabled [Bertillon, 1913; Moser et al., 1987; Goldblatt, 1988, 19901, and also on the social composition of the employed, as both the incidence of disabling illnesses and its effect on employment at older working ages are associated with social class [Fox et al., 1982; White, 1983; Fox and Shewry, 1988; Goldblatt, 19901. Thus, a number of factors must be presumed to have contributed to the differences shown in this paper. Taken together, these observations suggest that the principal influences on health selection are not socio-economic conditions themselves, but the numerous ways in which they can affect the identification of employed groups.

CONCLUSIONS

Ogle suggested that the mortality rates of those in employment are reduced by the selective effects of good health [Registrar General, 18851. The mechanisms put forward to explain this effect imply that it is one which should wear off as employed people are followed-up over time. The data presented here provide strong support for this suggestion. Not only do the mortality levels of those employed at census rise with subsequent follow-up, but this effect is strongly age-related. The latter appears to be a consequence of the development and increasing predominance of chronic diseases with increasing age. It has been suggested that the so-called healthy worker effect in occupational studies results from a number of factors. These include health selection, artifacts of under-reporting, and the circumstances of the employed. The first of these has been shown here to be important; the second is largely irrelevant to the present study, as internal comparisons have been used and because the study is of a generally high quality. As far as the third of these factors is concerned, it has been shown that the social background of the employed is unlikely to account for a major part of their low mortality (which does seem to persist to some degree when selection effects have worn off).

ACKNOWLEDGMENTS

The views expressed are those of the authors and are not necessarily those of OPCS. This work was supported by the Medical Research Council through grant number G8203453. Crown copyright is reserved.

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REFERENCES Baker RJ, Nelder JA (1978): “The GLIM System: Release 3.” Oxford: Numerical Algorithms Group. Berry G (1983): The analysis of mortality by the subject-years method. Biometrics 39:173-184. Bertillon J (1913): Occupational mortality and cause of death. Am Stat Assoc 13:457-465. Breslow NE, Day NE (1985): The standardized mortality ratio. In Sen PK (ed): “Biostatistics: Statistics in Biomedical Public Health and Environmental Science.” New York: Elsevier, pp 55-74. Breslow NE, Lubin JH, Marek P, Langholz BJ (1983): Multiplicative models and cohort analysis. J Am Stat Assoc 78:l-12. Carpenter LM (1987): Some observations on the healthy worker effect. Br J Ind Med 44:289-291. Clayton DG (1982): The analysis of prospective studies of disease epidemiology. Comm Stat A-Theory and Methods 11:2129-2155. Doll R (1952): The causes of death among gas-workers with special reference to cancer of the lung. Br J lnd Med 9:180-185. Doll R (1984): General discussion of “Expected Numbers in Cohort Studies.” In Gardner MJ (ed) “Expected Numbers in Cohort Studies. Proceedings of a Meeting at the MRC Environmental Epidemiology Unit.” Scientific Paper no. 6, Southampton, pp 45-50. Enterline PE (1964): The estimation of expected rates in occupational disease epidemiology. Public Health Rep 79:973-978. Enterline PE (1975): What do we expect from an occupational cohort? Not uniformly true for each cause of death. J Occup Med 17:127-128. Farr W (1885): In Humpreys NA fed) “Vital Statistics: A Memorial Volume of Selections from the Reports and Writings of William Farr.” London: Sanitary Institution. (reprinted, New Jersey: Scarecrow, 1975). Fox AJ (1979): The role of OPCS in occupational epidemiology: Some examples. Ann Occup Hyg 21:393-403. Fox AJ, Adelstein (1978): Occupational mortality: Work or way of life? J Epidemiol Community Health 32:73-78. Fox AJ, Collier PF (1976): Low mortality rates in industrial cohort studies due to selection for work and survival in the industry. Br J Prev SOCMed 30:225-230. Fox AJ, Goldblatt PO (1982): “Socio-demographic Mortality Differentials: Longitudinal Study 1971-75. LS no. 1 .” London: Her Majesty’s Stationery Office. Fox AJ, Shewry M (1988): New longitudinal insights into relationships between unemployment and mortality. Stress Med 4: 11-19. Fox AJ, Goldblatt PO, Adelstein AM (1982): Selection and mortality differentials. J Epidemiol Community Health 36:69-79. Gaffrey WR (1975): What do we expect from an occupational cohort? Cause specific mortality. J Occup Med 17:128. Gardner MI (ed.) (1984): “Expected Numbers in Cohort Studies. Proceedings of a Meeting at the MRC Environmental Epidemiology Unit.” Scientific Paper no. 6, Southampton. Goldblatt PO (1988): Changes in social class between 1971 and 1981: Could these affect mortality differences among men of working age? Population Trends 5 1:9-17. Goldblatt PO (1989): Mortality by social class, 1971-85. Population Trends 56:6-15. Goldblatt P (ed.) (1990): “Mortality and Social Organisation. LS no. 6.” London: Her Majesty’s Stationery Office. Goldblatt P, Fox J (1977): Household mortality from the OPCS Longitudinal Study. Population Trends 14:20-27. Goldsmith JR (1975): What do we expect from an occupational cohort? J Occup Med 17:126-127. Guy WA (1845): On the causes which determine the choice of an employment. J Stat SOC8:351-353. McCullagh P, Nelder JA (1983): “Generalized Linear Models.” London: Chapman Hall. McMichael AJ (1976): Standardized mortality ratios and the “healthy worker effect”: Scratching beneath the surface. J Occup Med 18:165-168. Moser KA, Goldblatt PO, Fox AJ, Jones DR (1987): Unemployment and mortality: Comparison of the 1971 and 1981 longitudinal study samples. Br Med J 294:86-90. Moser KA, Pugh H, Goldblatt PO (1988): Inequalities in women’s health: Looking at mortality differentials using an alternative approach. Br Med J 296 OPCS (1973): “Cohort Studies: New Developments. Stud al and Population Subjects No. 25.” London: Her Majesty’s Stationery Office.

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OPCS (1978): “Occupational Mortality: Decennial Supplement 1970-72. DS no. 1.” London: Her Majesty’s Stationery Office. OPCS (1986): “Occupational Mortality: Decennial Supplement 1979-80, 1982-83. DS no. 4.” London: Her Majesty’s Stationery Office. Osmond C, Gardner MJ (1982): Age, period, and cohort models applied to cancer mortality rates. Stat Med 1:245-259. Ostlin P (1988): Negative health selection into physically light occupations. J Epidemiol Community Health 42:152-156. Ostlin P (1989): “Occupational Career and Health: Methodological Considerations on the Healthy Worker Effect. ” Uppsala: Acta Universitatis Upsaliensis. Ostlin P (1990): Occupational history, self-reported chronic illness and mortality. J Epidemiol Community Health 44:12-16. Ramazzini B (1713): “De Morbis Artificium Diatriba.” Geneva. (reprinted, New York Hafner, 1964). Registrar General (1855): “Fourteenth Annual Report of the Registrar General.” London: Her Majesty’s Stationery Office. Registrar General (1875): “Supplement to the Thirty-Fifth Annual Report of the Registrar General.” London: Her Majesty’s Stationery Office. Registrar General (1885): “Supplement to the Forty-Fifth Annual Report of the Registrar General. ” London: Her Majesty’s Stationery Office. Registrar General (1923): “Supplement to the Seventy-Fifth Annual Report of the Registrar General.” London: His Majesty’s Stationery Office. Registrar General (1927): “Decennial Supplement, England and Wales, 1921 Part I1 Occupational Mortality, Fertility and Infant Mortality.” London: His Majesty’s Stationery Office. Shindell S, Weisberg RF, Geifer EE (1978): The “healthy worker effect”-fact or artifact? J Occup Med 20:807-811. Stevenson THC (1923): The social distribution of mortality from different causes in England and Wales. Biometrika XV:382-400. Weed DL (1986): Historical roots of the healthy worker effect. J Occup Med 28:343-347. Wen CP, Tsai SP, Gibson RL (1983): Anatomy of the healthy worker effect: A critical review. J Occup Med 25:283-289. White M (1983): ‘‘Long-term unemployment and labour markets. Report no. 622.” London: Policy Studies Institute. WHO (1969): “International Classification of Diseases, 8th Revision. ” Geneva: WHO. Wilcosky T, Wing S (1987): The healthy worker effect: selection of workers and work forces. Scand J Work Environ Health 13:70-72. Yule GU (1934): On some points relating to vital statistics, more especially statistics of occupational mortality. J Roy Stat SOC 97: 1-84.

APPENDIX Statistical models of the following functional form were fitted log 0, = log E,

+ f(ai,tj)

where i and j represented age and time period, respectively; ai and tj were the mid-points of these periods; 0, and E, were the corresponding observed and expected numbers of deaths; and f(a,t) a polynomial with terms: l,

a” tn

in which l, were constants and m and n were integers, representing different power transformations of age (a) and time (t), respectively. Models with values of m between 0 and 3 and n between -2 and 1 were considered. A Poisson error structure was assumed [Breslow et al., 1983; McCullagh and Nelder, 19831.

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A model was chosen from this family by only including non-zero terms, l,,, which gave the largest, statistically significant reductions in scaled deviance [Baker and Nelder, 19781. Under this definition of parsimony the model selected was one containing only two terms involving age and time. It can be written as: f(c,t) = -0.8437 - 0.8726(c3/t)

+ O.O549(~/t)~

where c is age, a, divided by 100 (that is to say age in fractions of a century) and t is length of follow-up (in years). Correspondingly, the fitted estimate of the SMR after t years of follow-up for those aged 100 X c at entry is given by the formula:

SMR

=

91.9

X

(0.4179)"3't'

X

(1 .056)(c'1)2.