Disability in Occupations in a National Sample
J. Paul Leigh, PhD, and James F. Fries, MD
While interest in income and education as covariates of arthritis has been expanding,'-'0 few studies have considered another important component of socioeconomic status: occupation.' 1-1 The neglect of research on occupation may represent a serious oversight. The substantial time devoted to employment over a lifetime suggests potential for exposure to harmful working conditions. We sought to identify broad occupational categories and specific occupations associated with the greatest amounts of musculoskeletal disability. A literature search located only two studies that attempted to rank occupations with national probability samples. Collins and Thomberry'3 used information from the National Health Interview Survey to analyze a number of health characteristics, including self-reported arthritis, of current workers within 15 broad occupation categories. Vingard et al. '4 used census data from Sweden and found farmers, construction workers, fire fighters, and mail carriers to be at high risk of osteoarthritis of the knees and hips. Unique aspects of our study include (1) rankings of many "longest held" jobs using a national probability sample and (2) use of analysis of covariance and multiple regression models that permit us to estimate occupation mean disability scores after adjusting for subjects' differences in age, education, attrition probability, and marital status.
in land-based segments and is representative of the civilian noninstitutionalized population residing in the 48 contiguous states.'6 NHEFS was a follow-up survey conducted during 1982 through 1984.17 NHEFS investigators attempted to survey 14 407 subjects from the original NHANES I; 12 545 were traced. Of these, 10 523 were reinterviewed and 2022 were found to have died. A total of 12 545 subjects were available for analysis. Our subsamples consisted of 6096 women and 3653 men for the occupation analyses and 6911 women and 4723 men for the attrition and mortality analyses. The subsamples were smaller than the 12 545 available in the NHEFS because, in the occupation analyses, we required that subjects (1) be alive in 1982 through 1984 and (2) provide answers to 25 disability questions and to questions pertaining to "longest-held" occupation: "What kind of work have you done for the longest period of time? What was your occupation or complete job title (for example, carpenter, secretary, electrical engineer)?" Ages of subjects in 1982 through 1984 ranged from 30 to 87. These age limits resulted from the NHEFS selecting only subjects who were at least 25 in 1971 through 1975.18 The dependent variable was a modified version of the disability index from the Stanford Health Assessment Questionnaire. The Health Assessment QuestionThe authors are with the Department of Med-
Methods Data Subsamples of persons were drawn from the National Health and Nutrition Examination Survey (NHANES I) Epidemiological Follow-up (NHEFS). The NHANES I is a multistage, stratified, probability sample of clusters of persons
icine, Stanford University School of Medicine, Stanford, Calif. J. Paul Leigh is also with the Department of Econonics, San Jose State University, San Jose, Calif. Requests for reprints should be sent to J. Paul Leigh, PhD, Department of Economics, San Jose State University, San Jose, CA 951920114. This paper was submitted to the Journal July 31, 1991, and accepted with revisions July 6, 1992.
American Journal of Public Health 1517
L-dg and Fs
naire is a multidimensional instrument developed by the Stanford Arthritis Center.19-21 The disability index represents eight categories: dressing and grooming, arising, eating, walking, hygiene, reach, grip strength, and outside activities. The reliability and validity of the questionnaire have been previously reported.1920 The information we used to construct a modified disability index included answers to 25 of 26 possible questions. The NHEFS question on the ability to "walk on a level surface" was not used in our analysis because of NHEFS coding errors. The possible answers to the 25 questions were coded as follows: 1 = no difficulty, 2 = modest dfficulty, 3 = some difficulty, 4 = impossible to do, and 7, 8, 9 = not answered or inappropriate answer. People with responses of 7, 8, or 9 were eliminated. The remaining numbers were recoded to be consistent with the 0-to-3 range of the Health Assessment Questionnaire disability index ("1" became "0," etc.). Since the modified disability index represents the mean of answers to the 25 questions, it is continuous and ranges between and includes 0 and 3. The 1982 through 1984 NHEFS used the 1980 US census occupation codes, while the NHANES I used the 1970 codes.22 Analyses were conducted on both the 12 broad census categories and the specific jobs. Not all 500 specific jobs were considered, however. Attention was restricted to jobs with 20 or more incumbents, which resulted in 61 female- and 45
male-specific jobs. 1518 American Journal of Public HealthN
Statistics Three methods were used to rank the occupations. The first involved a simple calculation of the mean disability index within each of the 12 categories and within 61 female- and 45 male-specific jobs. Men and women were separated because of well-known sex differences in disability.23 The second two methods involved applications of linear models. In the first model, multiple regression was applied. The dependent variable was the modified Health Assessment Questionnaire disability index, and the independent variables were continuous age, age squared, a binary married-spouse-present variable, years of schooling, an instrumental variable measuring the probability of attrition, and binary variables representing the occupations. Eleven binary variables were included in the analyses of the broad categories; 60 binaryvariables were included in the model for women and 44 binary variables in the male model. One binary occupation variable was eliminated to break the perfect collinearity.24 This multiple regression model is equivalent to an analysis of covariance model,25 26 and it allows for the calculation of mean disability values controlling for the linear associations of age, age squared, married, schooling, and attrition. To minimize the bias of attrition due to death over the follow-up, we applied an econometric technique.27-29 The technique involved the creation of an instrumental variable that attempted to measure the unobserved probability of dying over the 10 to 14 years of follow-up. A multiple
regression was run on all of the living and deceased subjects in the follow-up. The dependent variable indicated whether the subject died (1) or lived (0). This binary variable was then regresed on covariates measured at baseline (1971 through 1975) that were thought to influence the probability of dying before the follow-up. Baseline covariates included age, age squared, married spouse present, race, employment status, and dummies for 11 of the 12 broad occupation categories. The predicted values for the binary probability variable then became the values for the instrumental variable included in the disability regression.30 The binary variable-multiple regression approach was implemented with PCCARP,31 a statistical software package that correctly estimates standard errors calculated using cluster samples, such as the NHANES I or NHEFS.32 Geogaphic clusters may have correlated errors within each cluster.32 PCCARP accounts for the correlation by estimating a model with two error terms: one for the variation in geographic clusters and another for the variation across subjects. The resulting estimated coefficients are more efficient than would be obtained with ordinary least squares. The analysis of covariance approach was implemented with the SAS LSMEANS subroutine.26 SAS does not control for correlated errors within clusters. Results from both procedures were similar. This was not surpnsing since correlated errors do not affect the statistical property of unbiasedness.31 Most standard errors from PCCARP were slightly larger than those from SAS.
Resdts Table 1 presents results on the age and education levels within broad occupational groups. Evidence in Table 1, combined with numerous labor market studies, suggests that age and education are distributed unevenly across occupations.33 Table 2 presents regression results for living (0) and dying (1) over the follow-up esfimated with PCCARP. Covariates were measured at baseline. The 1970 occupation categories were assigned to 1980 categories using the census relationship manual.22 Among women, age squared, being Black, working as a laborer or operative, and being retired were found to be positively and significantly correlated with mortality, while age and years of schooling were found to be negNovember 1992, Vol. 82, No. 11
Disabity in Occupaions atively and significantly correlated. Among men, age squared; working as a transportation operative, other operative, or laborer; unemployed; and retired were found to be positively and significantly correlated; married spouse present was found to be negatively and significantly correlated with mortality (i.e., attrition). Tables 3 and 4 present results for women and men in the broad occupation categories with PCCARP corrected standard errors. The confidence intervals (CIs) are not for the distribution of disability index scores within any given occupation; these are confidence intervals for the occupation means of the disability index. The majority of people have disability indices outside these intervals. The ranking for women in Table 3 from highest to lowest disability index, when age, age squared, attrition, and married spouse present were included as covariates, was as follows: farming, no occupation, laborers, service, technicians, operatives, crafts workers, transportation operatives, professionals, sales workers, administrative support, and managers. The ranking for men in Table 4 was as follows: no occupation, farming, laborers, operatives, transportation operatives, crafts workers, service, technicians, managers, administrative support, sales, and professionals. These rankings were remarkably similar to those of the brief study by Collins and Thornbeny,13 who found high arthritis prevalence among currently employed women and men in private household employment, protective service, farming, precision repair, and laboring occupations. However, the Collins and Thornberry study did not control for any covariates. Results for the covariates for age, age squared, attrition, and married spouse present are omitted in the interest of brevity. Tables 5 and 6 present rankings for women and men in specific jobs. Highdisability jobs for women included nonconstruction laborers, farm workers, twisting machine operators, household servants, food supervisors, and dry cleaning operators. Low-disability jobs for women included sales workers for other commodities, child care workers, designers, order clerks, data entry keyers, inspectors, and kindergarten teachers. High-disability jobs for men included no occupation, machinery maintenance occupations, mining machine operators, nonconstruction laborers, bus drivers,
farm workers, shipping clerks, farmers, auto mechanics, and accountants. Lowdisability jobs for men included mechanic November 1992, Vol. 82, No. 11
supervisors, education administrators, meat cutters, civil engineers, supervisors not elsewhere classified, postal clerks, sales workers, clergy, and lawyers. The most notable difference when comparing these rankings with and without controlling for years of schooling (see columns 5 and 7 of Tables 5 and 6) involved female secondary school teachers. Before schooling enters as a covariate, female secondary school teachers rank far down the list in Table 5 at position 51. After schooling is entered as a covariate, female secondary school teachers rise to position 15.
Discussion Tables 3 through 6 provide preliminary rankings of "longest-held" occupations for women and men based on the average disability indices of incumbents. Limitations should be noted. First, people will change jobs over their lifetimes. For example, there may be safe jobs people turn to if they have become disabled from a hazardous job. Altemative measures of occupational exposures may, however, lead to even more serious biases. One altemative measure would rank jobs based on the mean disability indexof current job holders. But current job
holders would not fairly represent the toll of occupational hazards, since severe disability may lead to unemployment.9'10 One question in the NHEFS elicited responses from all people, whether currently working, retired, homemakers, or disabled. A second altemative measure would rankjobs, as reported in NHANES 1 (1971 through 1975), against the disability index as reported in 1982 through 1984. This measure also suffers a bias, however, since people may have changed jobs between 1971 and 1984. A disability index could not be constructed for 1971 through 1975 since NHANES I investigators did not ask the appropriate questions. A third altemative involves limiting the sample to those with the same current job in 1971 through 1975 and the "longestheld" job in 1982 through 1984. But this approach also creates problems. First, the US census changed the definition of many jobs between 1970 and 1980. Although a manual is available that attempts to indicate the relationships between the definitions, the attempts are imperfect.22 Second, some people may have moved into a job in 1971 through 1975 because of a disability from a prior "longest-held" job. Third, some people may have had a temporaryjob at the time of their 1971 through 1975 interview. By requiring a match beAmerican Journal of Public Health 1519
tegh and Fres
tween 1971 through 1975 and 1982 through 1984, persons who were employed in 1971 through 1975 jobs that they did not regard as "longest held" in 1982 through 1984 would be ignored in this third possible alternative analysis. Nevertheless, the broad occupation groups were analyzed by matching 1971 through 1975 and 1982 through 1984 broad categories (results are available from the authors). Results were very similar to those reported in Tables 3 and 4. Longitudinal data covering at least 20years, and indicating persons, jobs, and disability indices in each year, would avoid these problems; however, we are
1520 American Joumal of Public Health
unaware of any national probability sample with these characteristics. A second criticism suggests that the occupations associated with the greatest disability also kill a disproportionate number ofworkers. This criticism implies that the NHEFS would result in a biased sample in favor of an overrepresentation of jobs that do not injure joints or are in any way associated with arthritis. There are answers to this criticism, too. Job-related injury deaths are rare, occurring with a frequency of about 7 per 100 000 per year, placing them well behind nonoccupational heart disease, can-
cer, accidents, homicides, and suicides.34 The criticism would imply that some important occupations would not have enough respondents in 1982 through 1984 to be included in Tables 3 through 6. The occupations in Tables 3 and 4, however, include all possible broad categories, and specific occupations in Tables 5 and 6 account for over 70% of all employees in Tables 3 and 4. Moreover, 31 of the top 40 jobs with the highest mortality rates are included in Tables 5 and 6.35 Finally, we attempted to remove the bias due to mortality by including an instrumental attrition variable. November 1992, Vol. 82, No. 1 1
Dsaby in O _uadm
November 1992, Vol. 82, No. 1 1
American Journal of Public Health 1521
gh and Fres
Third, basing some estimates on as few as 20 members within an occupation may be invalid. This criticism also has answers. First, a higher limit of, say, 40 members would result in over half of the occupations in Tables 5 and 6 being 1522 American Joumal of Public Health
eliminated. Second, given the large sample sizes for the broad occupation categories, Tables 3 and 4 do not suffer from this criticism, yet they provide results consistent with the more fine-grained analyses in Tables 5 and 6.
Our results suggest that employees in
blue-collar jobs experience more disability than those in white-collar jobs. Moreover, Tables 3 and 4 suggest that the highest paid
white-collar jobs (e.g., professionals and managers) tend to have the lowest disabilNovember 1992, Vol. 82, No. 1 1
Disability in Occupations
ity scores and the lowest paid blue-collar jobs (e.g., laborers) have the highest disability scores. These trends are especially pronounced among men. The high levels of disability among blue-collar workers, especially those in jobs with low pay, are consistent with findings on occupations, low self-efficacy, and heart disease.36-4 The results are also consistent with those indicating that persons who leave blue-collar jobs are more likely to apply for and receive Social Security disability benefits, and to retire earlier, than white-collar employees.l 47,48 This could be due to the stress and injury associated with these jobs.49 Alternatively, persons in low-paying jobs may find that Social Security benefits are sufficiently generous to warrant a feigned disability.47 A number ofjobs may expose incumbents to risk of injury to joints and repetitive motions. Machinery maintenance workers, mining machine operators, laborers, shipping clerks, farm workers and owners, and auto mechanics are employed in occupations with high injury rates.35 Bus drivers, painters, twisting machine operators, assemblers, and packaging machine operators experience repetitive motions.5-52 In any case, the presence of high levels of disability in lower echelon jobs is not a unique finding. Strong evidence for similar occupations is available from En-
gland.53 The tables also suggest another finding for men andwomen: the no-occupation category is very high on all lists. Possibly, persons who do not list an occupation could have long-standing and severe disabilities that keep them from worldng.lO Second, some persons stating "no occupation" may have experienced long periods of unemployment that, in tum, may have deleterious effects on health.5455 Third, among women, the more able bodied may choose to work outside rather than in the home.56 Fourth, again among women, housework could itself be an occupation that could cause injuries to joints that would ultimately lead to disability. In Table 5, private household cleaners and servants rank third in disability out of 61. Education and arthritis are correlated.1-7 Nevertheless, education should not automatically be a covariate in this study. Inclusion depends on which social science theory is invoked. If the human capital theory is invoked, then education should be indluded as a covariate.57-59 The human capital view is that education endows people with health knowledge and November 1992, Vol. 82, No. 11
generally increases their efficiency in allocating resources to stay healthy. The institutional view, on the other hand, holds that it is the person'sjob that results in his or her health improving or deteriorating and that education merely serves as a surrogate measure of job characteristics.3641 The institutional view suggests that education should be excluded as a covariate, especially if job characteristics such as noise, heat, weather, and chemical exposures are not available in the data. Since we cannot reconcile these two theories here, estimates are presented that altematively include and exclude education. The data demonstrate that when education was included as a covariate, the top broad occupational categories (no occupation, laborers, farmers, and farm workers) were unaffected but some other occupations changed rank, including operatives, sales workers, and professionals. The contrast between these two models is minimal in Tables 5 and 6, which rank specific jobs. The rank correlation coefficients between the first two "rank" columns (4 and 6) are .891 in Table 5 and .986 in Table 6. Most people spend between 1000 and 2500 hours per year working. Jobs can expose people to risks of injuries, repetitive motions, noise, heat, cold, humidity, chemicals, and stress. Given the potential for exposure, job and disability associations deserve greater research attention. Future research efforts would be greatly assisted if
datawerecollectedonspecificenvironmental and psychosocial job characteristics such as those available in the University ofMichigan's Quality of Employment Surveys from 1973 and 1977.60 O
Acknowledgment This work was supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases grant AM 21393 to Arthritis, Rheumatism, and Aging Management Information System (ARAMIS).
References 1. Pincus T, Callahan LF, Burkhauser RV. Most chronic diseases are reported more frequently by individuals with fewer than 12 years of formal education in the age 18-64 United States population. J Chronic Dis. 1987;40:865-874. 2. Pincus T. Formal educational level-a marker for the importance of behavioral variables in the pathogenesis, morbidity, and mortality of most diseases?JRhewna-
toL 1988;15:1457-1460. 3. Deyo RA, DiehIl AK Psychosocial predictors of disability in patients with low back pain. JRhzewnatoL 1988;15:1557-1564. 4. Pincus T, Callahan LF. Formal education as a marker for increased mortality and
morbidity in rheumatoid arthritis. J Chronc Dis. 1985;38:978-984. 5. Callahan LF, Pincus T. Formal education level as a significant marker of clinical status in rheumatoid arthritis. Arthritis Rheum. 1988;31:1346-1357. 6. Nicassio PM, Waliston KA, Callahan LF, Herbert MA, Pincus T. Measurement of helplessness in rheumatoid arthritis: development of the Arthritis Helplessness Index. JRhJewnatoL 1985;12:462-467. 7. Callahan LF, Brooks RH, Pincus T. Further analysis of leamed helplessness in rheumatoid arthritis using a "Rheumatology Attitudes Index." JRheunatoL 1988; 15:418-426. 8. Mitchell JM, Burkhauser RV, Pincus T. The importance of age, education, and comorbidity in the substantial earnings losses of individuals with symmetric polyarthritis. Arnhitis RheunL 1988;31:348-357. 9. Mitchell JM, Butler JS. Arthritis and the earnings of men: an analysis incorporating selection bias. J Health Economics. 1986; 57:81-98. 10. Yelin EH. The myth of malingering: why individuals withdraw from work in the presence of illness. Milbank Q. 1986;64: 622-649. 11. Anderson JJ, Felson DT. Factors associated with osteoarthritis of the knee in the first national health and nutrition examination survey (NHANES I).Am JEpdemioL 1988;128:179-189. 12. Hopkins A. The social recognition of repetitive strain injuries: an Australian/ American comparison. Soc SciMed 1990; 30:365-372. 13. National Center for Health Statistics, Collins JG, Thornberry OT. Health characteristics of workers by occupation and sex: U.S. 1983-85.AdvData Vital Health Stat. April 25, 1989; no. 168. DHHS publication PHS 89-1250. 14. Vingard E, Alfredsson L, Goldie I, Hogstedt C. Occupation and osteoarthrosis of the hip and knee: a register-based cohort study. IntJEpidenmoL 1991;20:1025-1031. 15. Leigh JP, Fries JF. Occupation, income, and education as independent covariates of arthritis in four national probability samples. Arthritis Rhewn 1991;134:984-995. 16. Miller HW. Plan and Operation of the Health and Nutition Examination Swvey. Vital Health Stat [1J. 1979; no. 10A. DHEW publication PHS 79-1310. 17. Madans JH, Kleimnan JC, Cox CS, et al. Ten years after NHANES I: report of initial follow-up, 1982484. Public Health Rep.
1986;101:465-473. 18. National Center for Health Statistics, Cohen BB, Barbans HE, Cox CS, et al. Plan and Operation of the NHANES I Epidemiologic Follow-up Study, 1982-84. Vtal Health Stat [1]. 1987; no. 22. DHHS publication PHS 87-1324. 19. Fries JF, Spitz PW, Young DY. The dimensions of health outcomes: the health assessment questionnaire, disability and pain scales. JRhewnatoL 1982;9:789-793. 20. Fries JF, Spitz P, Kraines RG, Hohnan HR. Measurement of patient outcome in arthritis. Arthriis Rheum. 1980;23:137145. 21. Wolfe F, Kleinheksel SM, Cathey MA, Hawley DJ, Spitz PW, Fries JF. The cli-
American Journal of Public Health 1523
dgh and Fies ical value of the Stanford Health Assessment Questionnaire funcional disabilityindex in patients with rheumatoid arthritis. J RhewnatoL 1988;15:1480-1488. 22. The Relationship between the 1970 and 1980 Industry and Occupation Classfication Systems. Washington, DC: US Bureau of the Census; 1989. Technical paper 59. 23. Lawrence RC, Hochberg MC, Kelsey JL, et al. Estimates of the prevalence of selected arthritic and musculoskeletal diseases in the United States. J Rheuo toL
1989;16:427-441. 24. Maddala GS. Intoductin to Econometncs. New York, NY: Macmillan; 1988. 25. Gujarati DN. Basic Econometuics. San Francisco, CA: McGraw-Hill; 1988. 26. SAS Institute Inc. SAS User's Guide: Statistcs, Version S Edtio Cary, NC: SAS Institute; 1985. 27. Heckman JJ. Sample selection bias as a specification error. Economettica. 1979; 47:153-161. 28. Hsiao C. Ana4sis of Panel Data. New York, NY: Cambridge University Press; 1986. 29. Hausman JA, Wise D. Attrition bias in experimental and panel data: the Gary income maintenance experiment. Econometnca. 1979;47:455-473. 30. Olsen RJ. A least squares correction for selectivity bias. Econometrica. 1980;48: 1815-1820. 31. Fuller WA, Kennedy W, Schnell D, et al. PCC4RP. Ames, Iowa: Statistical Laboratory, Iowa State University; 1989. 32. Moulton BR. Random group effects and the precision of regression estimates. J Economerics. 1986;32:385-397. 33. Hamermesh DS, Rees A. 7he Economics of Work and Pay. New York, NY: Harper and Row; 1988. 34. Bell CA, Stout NA, Bender TR, Conroy CS, Crouse WE, Myers JR. Fatal occupational injuries in the U.S., 1980 through 1985. JAMA4. 1990;263:3047-3050. 35. Leigh JP. Estimates of the probability of job-related death in 347 occupations. J Occup Med. 1987;29:510-519. 36. Schnall PL, Pieper C, Schwartz JE. The relationship between job strain, workplace diastolic blood pressure, and left ventricu-
1524 American Journal of Public Health
37.
38.
39.
40.
41. 42.
lar mass index results of a case-control study. JAMA4. 1990;263:1929-1935. Karask RA, Theorel T, Schwartz JE. Job characteristics in relation to the prevalence of myocardial infarction in the U.S. Health Examination Survey (HES), and the Health and Nutrition Examination Survey (HANES). Am J Pksblic Health. 1988;78: 910-916. KarasekRA, BakerD, MarxerF, et al. Job decision latitude, job demands, and cardiovascular disease: a prospective study of Swedish men. Am J Public Health. 1981; 71:694-705. Karasek RA, Theoreil T. Heakty WoHk Strse, Prdutivify, and the Reconstiruction of Working L#e. New York, NY: Basic Books; 1990. Mathews KA, Cottington E, Talbott ED. Stressful work conditions and diastolic blood pressure among blue-collar factory workers. Am J Epidemol 1987;126:280290. Leigh JP. Occupational hazards and heart attacks. Soc Sci Med 1986;23:1181-1185. Murdrick NR. Predictors of disability among midlife men and women: differences by severity of impairment. J Com-
munity Heakl 1988;13:70-84. 43. Solomon GF, Amkrant AR. Psychoneuroendocrinological effects on the immune response: review. Annu Rev MicrobioL 1981;35:155-184. 44. Wiedenfeld SA, O'Leary A, Bandura A, Brown S, Levine S, Raska K. Impact of perceived self-efficacy in copingwith stressors on the components ofthe immune system. J Pers Soc PsychoL 1990;59:10821094. 45. Bandura A. Self-efficacy mechanism in physiological activation and health-promoting behavior. In: Madden J, Mattyssee S, Borchas J, eds. Adaptation, Leaming, andAffect. New York, NY: Raven Press; 1991. 46. O'Leary A. Stress, emotion, and human immune function. Psychol BulL 1990;108: 363-382. 47. Bound J, Waidmann T. Disabiity Transfers and the Labor Force Attachment of Older Men: Evidence ftrm the Histoncal Recordn Cambridge, Mass: National Bu-
reau of Economic Research; 1990. Working paper 3437. 48. Mitchell OS, Levine PB, Pozzebon S. Retirement differences by industry and occupation. Gerontologist. 1988;28:545-551. 49. Chirikos TN, Nestel G. Occupational differences in the ability of men to delay retirement.JHum Resources. 1991;26:1-26. 50. Cannon LF, Bemacki EJ, Walter SD. Personal and occupational factors associated with carpal tunnel syndrome. J Occup Med 1981;23:255-258. 51. Wieslander G, Norback D, Gothe J, Juhlin L. Carpal tunnel syndrome and exposure to vibration, repetitive wrist movements, and heavy manual work: a case referent study. BrJ Ind Med 1989;46:43-47. 52. Franklin GM, Haug J, Heyer N, Checkoway H, Peck N. Occupational carpal tunnel syndrome in Washington State, 19841988. Am J Public Health. 1991;81:741746. 53. Black D, Morris JN, Smith C, Townsend P, Whitehead M. Inequaliies in Healtkh 7he Black Report and the Health Divide. London, England: Penguin Books; 1988. 54. Ametz B, Brenner SD, Levi L, et al. Immune function in unemployed women. Psychosom Med 1987;49:3-12. 55. Cobb S, Kasl S. The Consequences ofJob Loss. Cincinnati, Ohio: National Institute for Occupational Safety and Health; 1977. NIOSH publication 77-224. 56. Blau FD, Ferber M. The Economics of Women, Men, and Work Englewood Cliffs, NJ: Prentice-Hall; 1986. 57. Grossman M. The Denand for Health A Theoretical and Empirical Investigation. New York, NY: Columbia University Press; 1972. NBER occasional paper 119. 58. Kenkel DS. Health behavior, health knowledge, and schooling. J Political Economy. 1991;99:287-305. 59. Kenkel DS. Consumer health information and the demand for medical care. Rev Economics Statistics. 1990;72:587-595. 60. Kalleberg AL.America at WoriL National Surveys of Employees and Employers. A Report to the U.S. Depanten of Labor. New York, NY: Social Science Research Council; 1986.
November 1992, Vol. 82, No. 11
J. Paul Leigh, PhD, and James F. Fries, MD
While interest in income and education as covariates of arthritis has been expanding,'-'0 few studies have considered another important component of socioeconomic status: occupation.' 1-1 The neglect of research on occupation may represent a serious oversight. The substantial time devoted to employment over a lifetime suggests potential for exposure to harmful working conditions. We sought to identify broad occupational categories and specific occupations associated with the greatest amounts of musculoskeletal disability. A literature search located only two studies that attempted to rank occupations with national probability samples. Collins and Thomberry'3 used information from the National Health Interview Survey to analyze a number of health characteristics, including self-reported arthritis, of current workers within 15 broad occupation categories. Vingard et al. '4 used census data from Sweden and found farmers, construction workers, fire fighters, and mail carriers to be at high risk of osteoarthritis of the knees and hips. Unique aspects of our study include (1) rankings of many "longest held" jobs using a national probability sample and (2) use of analysis of covariance and multiple regression models that permit us to estimate occupation mean disability scores after adjusting for subjects' differences in age, education, attrition probability, and marital status.
in land-based segments and is representative of the civilian noninstitutionalized population residing in the 48 contiguous states.'6 NHEFS was a follow-up survey conducted during 1982 through 1984.17 NHEFS investigators attempted to survey 14 407 subjects from the original NHANES I; 12 545 were traced. Of these, 10 523 were reinterviewed and 2022 were found to have died. A total of 12 545 subjects were available for analysis. Our subsamples consisted of 6096 women and 3653 men for the occupation analyses and 6911 women and 4723 men for the attrition and mortality analyses. The subsamples were smaller than the 12 545 available in the NHEFS because, in the occupation analyses, we required that subjects (1) be alive in 1982 through 1984 and (2) provide answers to 25 disability questions and to questions pertaining to "longest-held" occupation: "What kind of work have you done for the longest period of time? What was your occupation or complete job title (for example, carpenter, secretary, electrical engineer)?" Ages of subjects in 1982 through 1984 ranged from 30 to 87. These age limits resulted from the NHEFS selecting only subjects who were at least 25 in 1971 through 1975.18 The dependent variable was a modified version of the disability index from the Stanford Health Assessment Questionnaire. The Health Assessment QuestionThe authors are with the Department of Med-
Methods Data Subsamples of persons were drawn from the National Health and Nutrition Examination Survey (NHANES I) Epidemiological Follow-up (NHEFS). The NHANES I is a multistage, stratified, probability sample of clusters of persons
icine, Stanford University School of Medicine, Stanford, Calif. J. Paul Leigh is also with the Department of Econonics, San Jose State University, San Jose, Calif. Requests for reprints should be sent to J. Paul Leigh, PhD, Department of Economics, San Jose State University, San Jose, CA 951920114. This paper was submitted to the Journal July 31, 1991, and accepted with revisions July 6, 1992.
American Journal of Public Health 1517
L-dg and Fs
naire is a multidimensional instrument developed by the Stanford Arthritis Center.19-21 The disability index represents eight categories: dressing and grooming, arising, eating, walking, hygiene, reach, grip strength, and outside activities. The reliability and validity of the questionnaire have been previously reported.1920 The information we used to construct a modified disability index included answers to 25 of 26 possible questions. The NHEFS question on the ability to "walk on a level surface" was not used in our analysis because of NHEFS coding errors. The possible answers to the 25 questions were coded as follows: 1 = no difficulty, 2 = modest dfficulty, 3 = some difficulty, 4 = impossible to do, and 7, 8, 9 = not answered or inappropriate answer. People with responses of 7, 8, or 9 were eliminated. The remaining numbers were recoded to be consistent with the 0-to-3 range of the Health Assessment Questionnaire disability index ("1" became "0," etc.). Since the modified disability index represents the mean of answers to the 25 questions, it is continuous and ranges between and includes 0 and 3. The 1982 through 1984 NHEFS used the 1980 US census occupation codes, while the NHANES I used the 1970 codes.22 Analyses were conducted on both the 12 broad census categories and the specific jobs. Not all 500 specific jobs were considered, however. Attention was restricted to jobs with 20 or more incumbents, which resulted in 61 female- and 45
male-specific jobs. 1518 American Journal of Public HealthN
Statistics Three methods were used to rank the occupations. The first involved a simple calculation of the mean disability index within each of the 12 categories and within 61 female- and 45 male-specific jobs. Men and women were separated because of well-known sex differences in disability.23 The second two methods involved applications of linear models. In the first model, multiple regression was applied. The dependent variable was the modified Health Assessment Questionnaire disability index, and the independent variables were continuous age, age squared, a binary married-spouse-present variable, years of schooling, an instrumental variable measuring the probability of attrition, and binary variables representing the occupations. Eleven binary variables were included in the analyses of the broad categories; 60 binaryvariables were included in the model for women and 44 binary variables in the male model. One binary occupation variable was eliminated to break the perfect collinearity.24 This multiple regression model is equivalent to an analysis of covariance model,25 26 and it allows for the calculation of mean disability values controlling for the linear associations of age, age squared, married, schooling, and attrition. To minimize the bias of attrition due to death over the follow-up, we applied an econometric technique.27-29 The technique involved the creation of an instrumental variable that attempted to measure the unobserved probability of dying over the 10 to 14 years of follow-up. A multiple
regression was run on all of the living and deceased subjects in the follow-up. The dependent variable indicated whether the subject died (1) or lived (0). This binary variable was then regresed on covariates measured at baseline (1971 through 1975) that were thought to influence the probability of dying before the follow-up. Baseline covariates included age, age squared, married spouse present, race, employment status, and dummies for 11 of the 12 broad occupation categories. The predicted values for the binary probability variable then became the values for the instrumental variable included in the disability regression.30 The binary variable-multiple regression approach was implemented with PCCARP,31 a statistical software package that correctly estimates standard errors calculated using cluster samples, such as the NHANES I or NHEFS.32 Geogaphic clusters may have correlated errors within each cluster.32 PCCARP accounts for the correlation by estimating a model with two error terms: one for the variation in geographic clusters and another for the variation across subjects. The resulting estimated coefficients are more efficient than would be obtained with ordinary least squares. The analysis of covariance approach was implemented with the SAS LSMEANS subroutine.26 SAS does not control for correlated errors within clusters. Results from both procedures were similar. This was not surpnsing since correlated errors do not affect the statistical property of unbiasedness.31 Most standard errors from PCCARP were slightly larger than those from SAS.
Resdts Table 1 presents results on the age and education levels within broad occupational groups. Evidence in Table 1, combined with numerous labor market studies, suggests that age and education are distributed unevenly across occupations.33 Table 2 presents regression results for living (0) and dying (1) over the follow-up esfimated with PCCARP. Covariates were measured at baseline. The 1970 occupation categories were assigned to 1980 categories using the census relationship manual.22 Among women, age squared, being Black, working as a laborer or operative, and being retired were found to be positively and significantly correlated with mortality, while age and years of schooling were found to be negNovember 1992, Vol. 82, No. 11
Disabity in Occupaions atively and significantly correlated. Among men, age squared; working as a transportation operative, other operative, or laborer; unemployed; and retired were found to be positively and significantly correlated; married spouse present was found to be negatively and significantly correlated with mortality (i.e., attrition). Tables 3 and 4 present results for women and men in the broad occupation categories with PCCARP corrected standard errors. The confidence intervals (CIs) are not for the distribution of disability index scores within any given occupation; these are confidence intervals for the occupation means of the disability index. The majority of people have disability indices outside these intervals. The ranking for women in Table 3 from highest to lowest disability index, when age, age squared, attrition, and married spouse present were included as covariates, was as follows: farming, no occupation, laborers, service, technicians, operatives, crafts workers, transportation operatives, professionals, sales workers, administrative support, and managers. The ranking for men in Table 4 was as follows: no occupation, farming, laborers, operatives, transportation operatives, crafts workers, service, technicians, managers, administrative support, sales, and professionals. These rankings were remarkably similar to those of the brief study by Collins and Thornbeny,13 who found high arthritis prevalence among currently employed women and men in private household employment, protective service, farming, precision repair, and laboring occupations. However, the Collins and Thornberry study did not control for any covariates. Results for the covariates for age, age squared, attrition, and married spouse present are omitted in the interest of brevity. Tables 5 and 6 present rankings for women and men in specific jobs. Highdisability jobs for women included nonconstruction laborers, farm workers, twisting machine operators, household servants, food supervisors, and dry cleaning operators. Low-disability jobs for women included sales workers for other commodities, child care workers, designers, order clerks, data entry keyers, inspectors, and kindergarten teachers. High-disability jobs for men included no occupation, machinery maintenance occupations, mining machine operators, nonconstruction laborers, bus drivers,
farm workers, shipping clerks, farmers, auto mechanics, and accountants. Lowdisability jobs for men included mechanic November 1992, Vol. 82, No. 11
supervisors, education administrators, meat cutters, civil engineers, supervisors not elsewhere classified, postal clerks, sales workers, clergy, and lawyers. The most notable difference when comparing these rankings with and without controlling for years of schooling (see columns 5 and 7 of Tables 5 and 6) involved female secondary school teachers. Before schooling enters as a covariate, female secondary school teachers rank far down the list in Table 5 at position 51. After schooling is entered as a covariate, female secondary school teachers rise to position 15.
Discussion Tables 3 through 6 provide preliminary rankings of "longest-held" occupations for women and men based on the average disability indices of incumbents. Limitations should be noted. First, people will change jobs over their lifetimes. For example, there may be safe jobs people turn to if they have become disabled from a hazardous job. Altemative measures of occupational exposures may, however, lead to even more serious biases. One altemative measure would rank jobs based on the mean disability indexof current job holders. But current job
holders would not fairly represent the toll of occupational hazards, since severe disability may lead to unemployment.9'10 One question in the NHEFS elicited responses from all people, whether currently working, retired, homemakers, or disabled. A second altemative measure would rankjobs, as reported in NHANES 1 (1971 through 1975), against the disability index as reported in 1982 through 1984. This measure also suffers a bias, however, since people may have changed jobs between 1971 and 1984. A disability index could not be constructed for 1971 through 1975 since NHANES I investigators did not ask the appropriate questions. A third altemative involves limiting the sample to those with the same current job in 1971 through 1975 and the "longestheld" job in 1982 through 1984. But this approach also creates problems. First, the US census changed the definition of many jobs between 1970 and 1980. Although a manual is available that attempts to indicate the relationships between the definitions, the attempts are imperfect.22 Second, some people may have moved into a job in 1971 through 1975 because of a disability from a prior "longest-held" job. Third, some people may have had a temporaryjob at the time of their 1971 through 1975 interview. By requiring a match beAmerican Journal of Public Health 1519
tegh and Fres
tween 1971 through 1975 and 1982 through 1984, persons who were employed in 1971 through 1975 jobs that they did not regard as "longest held" in 1982 through 1984 would be ignored in this third possible alternative analysis. Nevertheless, the broad occupation groups were analyzed by matching 1971 through 1975 and 1982 through 1984 broad categories (results are available from the authors). Results were very similar to those reported in Tables 3 and 4. Longitudinal data covering at least 20years, and indicating persons, jobs, and disability indices in each year, would avoid these problems; however, we are
1520 American Joumal of Public Health
unaware of any national probability sample with these characteristics. A second criticism suggests that the occupations associated with the greatest disability also kill a disproportionate number ofworkers. This criticism implies that the NHEFS would result in a biased sample in favor of an overrepresentation of jobs that do not injure joints or are in any way associated with arthritis. There are answers to this criticism, too. Job-related injury deaths are rare, occurring with a frequency of about 7 per 100 000 per year, placing them well behind nonoccupational heart disease, can-
cer, accidents, homicides, and suicides.34 The criticism would imply that some important occupations would not have enough respondents in 1982 through 1984 to be included in Tables 3 through 6. The occupations in Tables 3 and 4, however, include all possible broad categories, and specific occupations in Tables 5 and 6 account for over 70% of all employees in Tables 3 and 4. Moreover, 31 of the top 40 jobs with the highest mortality rates are included in Tables 5 and 6.35 Finally, we attempted to remove the bias due to mortality by including an instrumental attrition variable. November 1992, Vol. 82, No. 1 1
Dsaby in O _uadm
November 1992, Vol. 82, No. 1 1
American Journal of Public Health 1521
gh and Fres
Third, basing some estimates on as few as 20 members within an occupation may be invalid. This criticism also has answers. First, a higher limit of, say, 40 members would result in over half of the occupations in Tables 5 and 6 being 1522 American Joumal of Public Health
eliminated. Second, given the large sample sizes for the broad occupation categories, Tables 3 and 4 do not suffer from this criticism, yet they provide results consistent with the more fine-grained analyses in Tables 5 and 6.
Our results suggest that employees in
blue-collar jobs experience more disability than those in white-collar jobs. Moreover, Tables 3 and 4 suggest that the highest paid
white-collar jobs (e.g., professionals and managers) tend to have the lowest disabilNovember 1992, Vol. 82, No. 1 1
Disability in Occupations
ity scores and the lowest paid blue-collar jobs (e.g., laborers) have the highest disability scores. These trends are especially pronounced among men. The high levels of disability among blue-collar workers, especially those in jobs with low pay, are consistent with findings on occupations, low self-efficacy, and heart disease.36-4 The results are also consistent with those indicating that persons who leave blue-collar jobs are more likely to apply for and receive Social Security disability benefits, and to retire earlier, than white-collar employees.l 47,48 This could be due to the stress and injury associated with these jobs.49 Alternatively, persons in low-paying jobs may find that Social Security benefits are sufficiently generous to warrant a feigned disability.47 A number ofjobs may expose incumbents to risk of injury to joints and repetitive motions. Machinery maintenance workers, mining machine operators, laborers, shipping clerks, farm workers and owners, and auto mechanics are employed in occupations with high injury rates.35 Bus drivers, painters, twisting machine operators, assemblers, and packaging machine operators experience repetitive motions.5-52 In any case, the presence of high levels of disability in lower echelon jobs is not a unique finding. Strong evidence for similar occupations is available from En-
gland.53 The tables also suggest another finding for men andwomen: the no-occupation category is very high on all lists. Possibly, persons who do not list an occupation could have long-standing and severe disabilities that keep them from worldng.lO Second, some persons stating "no occupation" may have experienced long periods of unemployment that, in tum, may have deleterious effects on health.5455 Third, among women, the more able bodied may choose to work outside rather than in the home.56 Fourth, again among women, housework could itself be an occupation that could cause injuries to joints that would ultimately lead to disability. In Table 5, private household cleaners and servants rank third in disability out of 61. Education and arthritis are correlated.1-7 Nevertheless, education should not automatically be a covariate in this study. Inclusion depends on which social science theory is invoked. If the human capital theory is invoked, then education should be indluded as a covariate.57-59 The human capital view is that education endows people with health knowledge and November 1992, Vol. 82, No. 11
generally increases their efficiency in allocating resources to stay healthy. The institutional view, on the other hand, holds that it is the person'sjob that results in his or her health improving or deteriorating and that education merely serves as a surrogate measure of job characteristics.3641 The institutional view suggests that education should be excluded as a covariate, especially if job characteristics such as noise, heat, weather, and chemical exposures are not available in the data. Since we cannot reconcile these two theories here, estimates are presented that altematively include and exclude education. The data demonstrate that when education was included as a covariate, the top broad occupational categories (no occupation, laborers, farmers, and farm workers) were unaffected but some other occupations changed rank, including operatives, sales workers, and professionals. The contrast between these two models is minimal in Tables 5 and 6, which rank specific jobs. The rank correlation coefficients between the first two "rank" columns (4 and 6) are .891 in Table 5 and .986 in Table 6. Most people spend between 1000 and 2500 hours per year working. Jobs can expose people to risks of injuries, repetitive motions, noise, heat, cold, humidity, chemicals, and stress. Given the potential for exposure, job and disability associations deserve greater research attention. Future research efforts would be greatly assisted if
datawerecollectedonspecificenvironmental and psychosocial job characteristics such as those available in the University ofMichigan's Quality of Employment Surveys from 1973 and 1977.60 O
Acknowledgment This work was supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases grant AM 21393 to Arthritis, Rheumatism, and Aging Management Information System (ARAMIS).
References 1. Pincus T, Callahan LF, Burkhauser RV. Most chronic diseases are reported more frequently by individuals with fewer than 12 years of formal education in the age 18-64 United States population. J Chronic Dis. 1987;40:865-874. 2. Pincus T. Formal educational level-a marker for the importance of behavioral variables in the pathogenesis, morbidity, and mortality of most diseases?JRhewna-
toL 1988;15:1457-1460. 3. Deyo RA, DiehIl AK Psychosocial predictors of disability in patients with low back pain. JRhzewnatoL 1988;15:1557-1564. 4. Pincus T, Callahan LF. Formal education as a marker for increased mortality and
morbidity in rheumatoid arthritis. J Chronc Dis. 1985;38:978-984. 5. Callahan LF, Pincus T. Formal education level as a significant marker of clinical status in rheumatoid arthritis. Arthritis Rheum. 1988;31:1346-1357. 6. Nicassio PM, Waliston KA, Callahan LF, Herbert MA, Pincus T. Measurement of helplessness in rheumatoid arthritis: development of the Arthritis Helplessness Index. JRhJewnatoL 1985;12:462-467. 7. Callahan LF, Brooks RH, Pincus T. Further analysis of leamed helplessness in rheumatoid arthritis using a "Rheumatology Attitudes Index." JRheunatoL 1988; 15:418-426. 8. Mitchell JM, Burkhauser RV, Pincus T. The importance of age, education, and comorbidity in the substantial earnings losses of individuals with symmetric polyarthritis. Arnhitis RheunL 1988;31:348-357. 9. Mitchell JM, Butler JS. Arthritis and the earnings of men: an analysis incorporating selection bias. J Health Economics. 1986; 57:81-98. 10. Yelin EH. The myth of malingering: why individuals withdraw from work in the presence of illness. Milbank Q. 1986;64: 622-649. 11. Anderson JJ, Felson DT. Factors associated with osteoarthritis of the knee in the first national health and nutrition examination survey (NHANES I).Am JEpdemioL 1988;128:179-189. 12. Hopkins A. The social recognition of repetitive strain injuries: an Australian/ American comparison. Soc SciMed 1990; 30:365-372. 13. National Center for Health Statistics, Collins JG, Thornberry OT. Health characteristics of workers by occupation and sex: U.S. 1983-85.AdvData Vital Health Stat. April 25, 1989; no. 168. DHHS publication PHS 89-1250. 14. Vingard E, Alfredsson L, Goldie I, Hogstedt C. Occupation and osteoarthrosis of the hip and knee: a register-based cohort study. IntJEpidenmoL 1991;20:1025-1031. 15. Leigh JP, Fries JF. Occupation, income, and education as independent covariates of arthritis in four national probability samples. Arthritis Rhewn 1991;134:984-995. 16. Miller HW. Plan and Operation of the Health and Nutition Examination Swvey. Vital Health Stat [1J. 1979; no. 10A. DHEW publication PHS 79-1310. 17. Madans JH, Kleimnan JC, Cox CS, et al. Ten years after NHANES I: report of initial follow-up, 1982484. Public Health Rep.
1986;101:465-473. 18. National Center for Health Statistics, Cohen BB, Barbans HE, Cox CS, et al. Plan and Operation of the NHANES I Epidemiologic Follow-up Study, 1982-84. Vtal Health Stat [1]. 1987; no. 22. DHHS publication PHS 87-1324. 19. Fries JF, Spitz PW, Young DY. The dimensions of health outcomes: the health assessment questionnaire, disability and pain scales. JRhewnatoL 1982;9:789-793. 20. Fries JF, Spitz P, Kraines RG, Hohnan HR. Measurement of patient outcome in arthritis. Arthriis Rheum. 1980;23:137145. 21. Wolfe F, Kleinheksel SM, Cathey MA, Hawley DJ, Spitz PW, Fries JF. The cli-
American Journal of Public Health 1523
dgh and Fies ical value of the Stanford Health Assessment Questionnaire funcional disabilityindex in patients with rheumatoid arthritis. J RhewnatoL 1988;15:1480-1488. 22. The Relationship between the 1970 and 1980 Industry and Occupation Classfication Systems. Washington, DC: US Bureau of the Census; 1989. Technical paper 59. 23. Lawrence RC, Hochberg MC, Kelsey JL, et al. Estimates of the prevalence of selected arthritic and musculoskeletal diseases in the United States. J Rheuo toL
1989;16:427-441. 24. Maddala GS. Intoductin to Econometncs. New York, NY: Macmillan; 1988. 25. Gujarati DN. Basic Econometuics. San Francisco, CA: McGraw-Hill; 1988. 26. SAS Institute Inc. SAS User's Guide: Statistcs, Version S Edtio Cary, NC: SAS Institute; 1985. 27. Heckman JJ. Sample selection bias as a specification error. Economettica. 1979; 47:153-161. 28. Hsiao C. Ana4sis of Panel Data. New York, NY: Cambridge University Press; 1986. 29. Hausman JA, Wise D. Attrition bias in experimental and panel data: the Gary income maintenance experiment. Econometnca. 1979;47:455-473. 30. Olsen RJ. A least squares correction for selectivity bias. Econometrica. 1980;48: 1815-1820. 31. Fuller WA, Kennedy W, Schnell D, et al. PCC4RP. Ames, Iowa: Statistical Laboratory, Iowa State University; 1989. 32. Moulton BR. Random group effects and the precision of regression estimates. J Economerics. 1986;32:385-397. 33. Hamermesh DS, Rees A. 7he Economics of Work and Pay. New York, NY: Harper and Row; 1988. 34. Bell CA, Stout NA, Bender TR, Conroy CS, Crouse WE, Myers JR. Fatal occupational injuries in the U.S., 1980 through 1985. JAMA4. 1990;263:3047-3050. 35. Leigh JP. Estimates of the probability of job-related death in 347 occupations. J Occup Med. 1987;29:510-519. 36. Schnall PL, Pieper C, Schwartz JE. The relationship between job strain, workplace diastolic blood pressure, and left ventricu-
1524 American Journal of Public Health
37.
38.
39.
40.
41. 42.
lar mass index results of a case-control study. JAMA4. 1990;263:1929-1935. Karask RA, Theorel T, Schwartz JE. Job characteristics in relation to the prevalence of myocardial infarction in the U.S. Health Examination Survey (HES), and the Health and Nutrition Examination Survey (HANES). Am J Pksblic Health. 1988;78: 910-916. KarasekRA, BakerD, MarxerF, et al. Job decision latitude, job demands, and cardiovascular disease: a prospective study of Swedish men. Am J Public Health. 1981; 71:694-705. Karasek RA, Theoreil T. Heakty WoHk Strse, Prdutivify, and the Reconstiruction of Working L#e. New York, NY: Basic Books; 1990. Mathews KA, Cottington E, Talbott ED. Stressful work conditions and diastolic blood pressure among blue-collar factory workers. Am J Epidemol 1987;126:280290. Leigh JP. Occupational hazards and heart attacks. Soc Sci Med 1986;23:1181-1185. Murdrick NR. Predictors of disability among midlife men and women: differences by severity of impairment. J Com-
munity Heakl 1988;13:70-84. 43. Solomon GF, Amkrant AR. Psychoneuroendocrinological effects on the immune response: review. Annu Rev MicrobioL 1981;35:155-184. 44. Wiedenfeld SA, O'Leary A, Bandura A, Brown S, Levine S, Raska K. Impact of perceived self-efficacy in copingwith stressors on the components ofthe immune system. J Pers Soc PsychoL 1990;59:10821094. 45. Bandura A. Self-efficacy mechanism in physiological activation and health-promoting behavior. In: Madden J, Mattyssee S, Borchas J, eds. Adaptation, Leaming, andAffect. New York, NY: Raven Press; 1991. 46. O'Leary A. Stress, emotion, and human immune function. Psychol BulL 1990;108: 363-382. 47. Bound J, Waidmann T. Disabiity Transfers and the Labor Force Attachment of Older Men: Evidence ftrm the Histoncal Recordn Cambridge, Mass: National Bu-
reau of Economic Research; 1990. Working paper 3437. 48. Mitchell OS, Levine PB, Pozzebon S. Retirement differences by industry and occupation. Gerontologist. 1988;28:545-551. 49. Chirikos TN, Nestel G. Occupational differences in the ability of men to delay retirement.JHum Resources. 1991;26:1-26. 50. Cannon LF, Bemacki EJ, Walter SD. Personal and occupational factors associated with carpal tunnel syndrome. J Occup Med 1981;23:255-258. 51. Wieslander G, Norback D, Gothe J, Juhlin L. Carpal tunnel syndrome and exposure to vibration, repetitive wrist movements, and heavy manual work: a case referent study. BrJ Ind Med 1989;46:43-47. 52. Franklin GM, Haug J, Heyer N, Checkoway H, Peck N. Occupational carpal tunnel syndrome in Washington State, 19841988. Am J Public Health. 1991;81:741746. 53. Black D, Morris JN, Smith C, Townsend P, Whitehead M. Inequaliies in Healtkh 7he Black Report and the Health Divide. London, England: Penguin Books; 1988. 54. Ametz B, Brenner SD, Levi L, et al. Immune function in unemployed women. Psychosom Med 1987;49:3-12. 55. Cobb S, Kasl S. The Consequences ofJob Loss. Cincinnati, Ohio: National Institute for Occupational Safety and Health; 1977. NIOSH publication 77-224. 56. Blau FD, Ferber M. The Economics of Women, Men, and Work Englewood Cliffs, NJ: Prentice-Hall; 1986. 57. Grossman M. The Denand for Health A Theoretical and Empirical Investigation. New York, NY: Columbia University Press; 1972. NBER occasional paper 119. 58. Kenkel DS. Health behavior, health knowledge, and schooling. J Political Economy. 1991;99:287-305. 59. Kenkel DS. Consumer health information and the demand for medical care. Rev Economics Statistics. 1990;72:587-595. 60. Kalleberg AL.America at WoriL National Surveys of Employees and Employers. A Report to the U.S. Depanten of Labor. New York, NY: Social Science Research Council; 1986.
November 1992, Vol. 82, No. 11
