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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Objective

To generate hypotheses regarding occupational exposures that may cause systemic autoimmune diseases.

Methods

Based on examination of US death certificates, we identified deaths in 26 states for which a cause was listed as rheumatoid arthritis (RA) (n = 36,178), systemic lupus erythematosus (SLE) (n = 7,241), systemic sclerosis (n = 5,642), or other systemic autoimmune disease (n = 4,270). Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to estimate associations between occupation and death from any systemic autoimmune disease, and from RA, SLE, and systemic sclerosis, specifically. Additionally, we estimated risks associated with occupational exposures, which were assigned using job-exposure matrices.

Results

A broad array of occupations was associated with death from systemic autoimmune diseases, including several of a priori interest. Farming occupation was associated with death from any systemic autoimmune disease (OR 1.3 [95% CI 1.2–1.4]), and increased risk was also seen with occupational exposure to animals and pesticides. Several industrial occupations were associated with death from any systemic autoimmune disease, including mining machine operators (OR 1.3 [95% CI 1.1–1.5]), miscellaneous textile machine operators (OR 1.2 [95% CI 1.0–1.4]), and hand painting, coating, and decorating occupations (OR 1.8 [95% CI 1.0–2.9]). These occupations were also significantly associated with death from the specific autoimmune diseases examined. Certain occupations entailing exposure to the public, such as teachers, were associated with systemic autoimmune disease–related death, whereas others, such as waiters and waitresses, were not.

Conclusion

Our results suggest that death from systemic autoimmune diseases may be associated with occupational exposures encountered in farming and industry. The hypotheses generated in this study provide leads for future research on determinants of these diseases.

Autoimmune diseases are characterized by immunologic responses to constituents of the body's own tissues (1). More than 8 million Americans (1 of every 31) have autoimmune diseases, and >200,000 cases are diagnosed every year (2). Furthermore, autoimmune diseases are among the top 10 leading causes of death in women younger than 65 years of age, indicating that these diseases have a large impact on potentially productive years of life lost (1). Systemic autoimmune diseases are those that affect multiple organs and include rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and systemic sclerosis (SSc; scleroderma), among others.

Beyond the overwhelming evidence that women are affected by autoimmune diseases at much greater rates than men, the etiology of these diseases is not well understood. Twin studies suggest a genetic component to the epidemiology of these diseases but have also revealed that environmental risk factors are important (3, 4). Several risk factors have been proposed, including smoking (5) and infectious agents such as the Epstein-Barr virus (6, 7).

Several occupational exposures have been linked to systemic autoimmune diseases; some appear to cause a generalized autoimmune response, while others may have greater specificity for a certain disease. Occupational silica exposure has been associated with SSc (8–12), SLE (13–15), and RA (16, 17) (for review, see ref. 18) and may promote autoimmunity by causing chronic inflammation and immune stimulation (18). Farming occupation has also been linked to autoimmune diseases (19, 20), possibly as a result of exposure to pesticides, which have been associated with serologic markers of autoimmunity (21). However, pesticide exposure was not associated with RA among women in a cohort of pesticide applicators and their spouses (22). Teaching occupation has been associated with rheumatic diseases as a group (23) and with some specific autoimmune diseases, such as multiple sclerosis (23), Sjögren's syndrome (23), and systemic sclerosis (9); however, a study of SLE did not reveal an association with teaching occupation (24). Solvents have been associated with SSc in several studies (9, 25–27) and with RA in one study (28) but not another (22). Development of SLE has been associated with occupational exposure to metals, including mercury (24). Mercury exposure has also been linked to SSc (29).

For this study, we examined potential associations of occupation and occupational exposures with the risk of mortality from systemic autoimmune diseases. Using US death certificate data on usual lifetime occupation, we attempted to infer occupational risk factors for the development of systemic autoimmune diseases in order to generate hypotheses for future studies.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Patients and controls.

Data for this project were obtained using death certificates from 26 US states (northeast: Maine, New Hampshire, New Jersey, Rhode Island, and Vermont; north central: Indiana, Kansas, Missouri, Nebraska, Ohio, and Wisconsin; south: Georgia, Kentucky, North Carolina, Oklahoma, South Carolina, Tennessee, and West Virginia; west: Alaska, Colorado, Hawaii, Idaho, Nevada, New Mexico, Utah, and Washington) from 1984 to 1998. Different states contributed data for all or part of the study period. This data set was developed by the National Cancer Institute, the National Institute for Occupational Safety and Health, and the National Center for Health Statistics (30).

Coding procedure.

International Classification of Diseases, Ninth Revision (ICD-9) codes listed as underlying or contributing causes of death were used to identify cases of SLE (ICD-9 code 710.0; n = 7,241), SSc (code 710.1; n = 5,642), Sjögren's syndrome (code 710.2; n = 631), dermatomyositis (code 710.3; n = 704), polymyositis (code 710.4; n = 1,713), RA (code 714.0–714.2; n = 36,178), unspecified connective tissue disorder (code 710.9, n = 1,150), and unspecified inflammatory polyarthropathy (code 714.9; n = 107). These disease types with known or suspected systemic autoimmune disease etiology encompassed our case group of “any systemic autoimmune disease.”

Control subjects were randomly selected from all deaths except those attributed to systemic autoimmune diseases (listed above) and were frequency-matched to all case types combined by age (in 5-year groups), sex, race (white, black, or other), year of death, and geographic region of US residence at the time of death (northeast, north central, south, or west). Five control subjects per case were selected. Only decedents at least 25 years of age at the time of death were included in this study in order to allow for the potential of at least 5 years' duration of working history before death.

Each decedent's “usual occupation” (the occupation held for the longest period of time in the decedent's life) as reported on the death certificate (31) was coded by the state health departments according to the 1980 US Census Bureau 3-digit classification system, which includes 231 industries and 509 occupations (32). Additionally, we assigned specific exposures based on occupation and industry titles using job-exposure matrices (JEMs) developed by an industrial hygienist (MD). Exposure assignments included intensity (estimated concentration of exposure), probability (likelihood that a job entailed an exposure), and confidence (certainty of exposure assignment). Specific occupational exposures assigned by the JEMs included asbestos, solvents, benzene, silica, pesticides, inorganic dust, lead fumes, lead dust, metal dust, nitrosamines, nitrogen oxides, polycyclic aromatic hydrocarbons, ionizing radiation, and wood dust. Occupations that entailed relatively large amounts of exposure to the public (e.g., teachers, health professionals, waiters and waitresses, some sales occupations, and clergy) or animals (e.g., biologists, agricultural occupations, and veterinarians) were assigned on a yes/no basis using JEMs specific to those exposures.

A numeric score for socioeconomic status (SES), derived from the scoring manual by Green (33), was created based on the occupation reported on the death certificate. These scores were grouped into tertiles (low SES = 21–49; medium SES = 50–51; high SES = 52–100). In general, individuals with the lowest scores were unskilled workers and laborers, those with medium scores were semiskilled, and the high scorers were skilled workers, managers, and professionals.

Statistical analysis.

Unconditional logistic regression was performed using SAS version 9.1 software (SAS Institute, Cary, NC). We estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) to describe the relationship between specific occupations and death from any systemic autoimmune disease, as well as from RA, SLE, or SSc, specifically. Decedents with “unknown occupation” listed on their death certificates were excluded from the analyses (1.2% of cases and 1.5% of controls). The reference exposure category for this analysis was all other occupations combined. Because of the large number of occupational titles analyzed (n = 509), only those that were significantly associated with systemic autoimmune disease death at an alpha level of 0.05 for any of the disease outcomes are presented.

Additionally, we estimated the risk of death from systemic autoimmune diseases associated with specific occupational exposures (intensity level, or yes/no), as assigned by exposure-specific JEMs, for RA, SLE, and SSc, and any systemic autoimmune disease. Again, to simplify presentation of the results, we display only the results for exposure intensity (low, medium, and high) of agents that showed significant associations in the highest exposure level and significant trends with increasing exposure levels; for all other agents, we display results for yes/no exposure. All analyses were adjusted with indicator variables for the matching factors (age [25–44 years, 45–64 years, ≥65 years], sex [dichotomous], race [black, white, other], death year [1984–1998], and region [northeast, north central, south, west]), as well as SES. Analyses were also run separately for men and women to look for sex-specific effects.

We conducted more specific analyses of agriculture-related occupations (farmers, farm managers, farm workers, and other types of farming jobs) by estimating the risks for these occupations separately for the crop and livestock industries compared with all other industries as the reference group.

We conducted several subanalyses to check the sensitivity of our results to potential sources of misclassification. For occupational exposures with assigned information on probability, intensity, and confidence, we conducted subanalyses in which we excluded occupational exposures that were classified as low confidence. We also conducted analyses of 4 levels of exposure probability (no [referent], low, medium, or high). In addition, we conducted subanalyses including only those cases with a systemic autoimmune disease listed as the primary cause of death (excluding cases in which a systemic autoimmune disease was listed as solely a contributing cause of death).

In order to provide insight into whether occupational exposures were associated with the incidence of autoimmune diseases or instead caused or accelerated death in persons who already had autoimmune diseases, we conducted subanalyses limited to deaths that occurred after 65 years of age. We hypothesized that because these decedents were beyond the typical retirement age when they died, their usual occupations may have been associated with disease incidence rather than rapid acceleration of their deaths.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Demographic characteristics of the case group were consistent with previous profiles of patients with systemic autoimmune diseases (Table 1) (34, 35). Total cases combined and controls were distributed identically for the matching factors, but distributions within individual diseases showed some variation. Patients in the youngest age category were more likely to have died of SLE than of the other diseases, and the oldest decedents were more likely to have died of RA than of the other diseases. Female patients were more likely to have died of SLE compared with the other diseases, and male patients were more likely to have died of RA. White patients were more likely to have died of RA compared with the other diseases, and African American patients were at greater risk of dying of SLE. Patients, particularly those with SSc, were more likely than control subjects to have been of higher socioeconomic status. The most common underlying cause of death in patients was musculoskeletal and connective tissue diseases (which encompasses all of the systemic autoimmune diseases). Control subjects were most likely to have had circulatory system diseases and neoplasms listed as the underlying cause of death.

Table 1. Demographic characteristics of the decedents selected for this study*
 Controls (n = 264,569)All cases (n = 52,919)RA (n = 36,178)SLE (n = 7,241)SSc (n = 5,642)Other (n = 4,270)
  • *

    Values are the percent. RA = rheumatoid arthritis; SLE = systemic lupus erythematosus; SSc = systemic sclerosis.

  • Note that one death may be included in more than one case type.

  • Includes deaths from Sjögren's syndrome (n = 631), dermatomyositis (n = 704), polymyositis (n = 1,713), unspecified connective tissue disease (n = 1,150), and unspecified inflammatory polyarthropathy (n = 107).

Age, years      
 25–44550.723108
 45–64191914323525
 65+757585455567
Sex      
 Male272730182329
 Female737370827771
Race      
 White888893728282
 African American11116261616
 Other111222
Region of occurrence      
 Northeast131313121713
 North central363637313237
 South373736423736
 West141414151414
Socioeconomic status      
 Low282626272525
 Medium363738343331
 High373836394243
Marital status      
 Single8871388
 Married424542515451
 Widowed413944252832
 Divorced98711108
 Unknown0.20.10.10.20.10.2
Occupational title and industry available      
 Yes98.598.898.898.898.999
 No1.51.21.21.21.11
Underlying cause of death      
 Infectious diseases0.30.60.50.90.50.7
 Neoplasms27995610
 Endocrine, nutritional, and metabolic diseases3.72.52.82.20.92.1
 Blood diseases0.50.80.90.60.30.8
 Mental disorders1.40.81.00.40.20.5
 Nervous system and sense organs diseases3.12.732.41.32.1
 Circulatory system diseases443339201522
 Respiratory system diseases9.01013558
 Digestive system diseases3.74.34.6434
 Genitourinary system diseases2.02.02.41.30.81.1
 Skin diseases0.20.50.60.50.20.2
 Musculoskeletal and connective tissue diseases0.23322576748
 Congenital anomalies0.20.10.10.20.020.1
 Injury and poisoning4.20.91.00.80.60.8
 Ill-defined conditions and other causes of death0.90.040.040.10.020.02

ORs and 95% CIs for associations between specific occupations and death from the outcomes we studied are shown in Tables 2 and 3. Several occupations entailing exposure to the public, including registered nurses, several teacher occupations, child care workers, receptionists, bank tellers, and elevator operators, were associated with increased risk of death from any or specific systemic autoimmune diseases; however, our finding of increased risk of systemic autoimmune diseases was not consistent across all job titles associated with exposure to the public. Several occupations involving food preparation and service were inversely associated with any systemic autoimmune disease, and waiters and waitresses in particular were at decreased risk of death from RA, SLE, and SSc.

Table 2. Associations indicating increased risk between specific occupations and death from systemic autoimmune diseases*
Occupation/SOC codeControls (n = 260,632)All cases (n = 52,277)OR (95% CI)RA cases (n = 35,730)OR (95% CI)SLE cases (n = 7,153)OR (95% CI)SSc cases (n = 5,578)OR (95% CI)
  • *

    Except where indicated otherwise, values are the percent. The occupations presented entailed significantly increased risk (alpha level of 0.05) in at least 1 outcome category (all cases combined, rheumatoid arthritis [RA], systemic lupus erythematosus [SLE], or systemic sclerosis [SSc]) and were represented by at least 5 subjects. SOC = standard occupational classification; OR = odds ratio; 95% CI = 95% confidence interval; NEC = not elsewhere classified.

  • Adjusted for age, sex, race, region, year of death, and socioeconomic status.

Other financial officers/250.150.181.18 (0.94– 1.48)0.161.15 (0.87– 1.52)0.180.95 (0.54– 1.67)0.301.64 (1.00– 2.67)
Mechanical engineers/570.100.121.14 (0.87– 1.50)0.110.98 (0.70– 1.37)0.101.52 (0.71– 3.24)0.182.04 (1.08– 3.86)
Registered nurses/951.51.61.09 (1.01– 1.17)1.41.05 (0.96– 1.16)2.21.23 (1.04– 1.45)1.90.99 (0.81– 1.21)
Physical therapists/1030.020.021.34 (0.73– 2.48)0.021.05 (0.45– 2.48)0.072.67 (1.03– 6.92)0.020.72 (0.10– 5.27)
Teachers, elementary school/1562.53.11.23 (1.16– 1.30)3.11.25 (1.17– 1.34)3.21.21 (1.05– 1.39)3.31.18 (1.01– 1.38)
Teachers, secondary school/1570.260.351.30 (1.10– 1.53)0.331.27 (1.04– 1.55)0.291.08 (0.69– 1.68)0.451.52 (1.01– 2.27)
Teachers, special education/1580.020.031.92 (1.10– 3.37)0.031.75 (0.84– 3.62)0.041.91 (0.58– 6.36)0.052.35 (0.72– 7.66)
Counselors, educational and vocational/1630.050.071.55 (1.07– 2.23)0.061.50 (0.93– 2.42)0.131.64 (0.81– 3.30)0.071.01 (0.37– 2.75)
Social workers/1740.240.261.04 (0.87– 1.26)0.170.84 (0.65– 1.10)0.551.40 (1.00– 1.96)0.360.98 (0.62– 1.53)
Artists, performers, and related workers, NEC/1940.030.031.14 (0.68– 1.91)0.020.77 (0.35– 1.67)0.112.90 (1.37– 6.15)0.051.39 (0.44– 4.43)
Dental hygienists/2040.020.021.59 (0.85– 2.97)0.021.72 (0.80– 3.71)0.072.98 (1.14– 7.83)0.020.80 (0.11– 5.91)
Health technologists and technicians, NEC/2080.060.091.42 (1.02– 1.96)0.040.89 (0.52– 1.51)0.221.90 (1.11– 3.24)0.181.81 (0.95– 3.45)
Secretaries/3132.62.81.10 (1.03– 1.16)2.61.10 (1.02– 1.18)3.21.02 (0.88– 1.17)3.91.23 (1.06– 1.42)
Receptionists/3190.210.251.19 (0.98– 1.44)0.190.98 (0.76– 1.26)0.381.32 (0.89– 1.96)0.431.85 (1.23– 2.80)
Bookkeepers, accounting, and auditing clerks/3371.21.31.11 (1.02– 1.21)1.31.19 (1.07– 1.31)1.10.89 (0.71– 1.13)1.51.08 (0.86– 1.35)
Telephone operators/3480.400.431.06 (0.92– 1.23)0.471.21 (1.03– 1.43)0.340.81 (0.53– 1.22)0.450.96 (0.64– 1.43)
Bank tellers/3830.140.211.47 (1.18– 1.82)0.171.39 (1.06– 1.83)0.251.33 (0.82– 2.17)0.321.72 (1.07– 2.78)
Teachers' aides/3870.090.091.01 (0.74– 1.38)0.030.51 (0.28– 0.94)0.321.59 (1.02– 2.48)0.221.33 (0.74– 2.39)
Cooks, private household/4040.030.041.50 (0.91– 2.46)0.052.28 (1.33– 3.88)0.030.76 (0.18– 3.15)0
Private household cleaners and servants/4071.91.81.01 (0.94– 1.09)1.71.09 (1.00– 1.20)2.31.04 (0.88– 1.23)1.70.94 (0.76– 1.16)
Firefighting occupations/4170.070.081.07 (0.77– 1.49)0.070.92 (0.61– 1.39)0.040.71 (0.22– 2.24)0.162.30 (1.17– 4.52)
Dental assistants/4450.040.061.45 (0.98– 2.14)0.061.68 (1.05– 2.70)0.070.96 (0.38– 2.39)0.071.20 (0.44– 3.28)
Elevator operators/4540.020.031.49 (0.86– 2.60)0.041.92 (1.07– 3.47)0.010.80 (0.11– 5.90)0.021.02 (0.14– 7.42)
Barbers/4570.100.111.22 (0.92– 1.61)0.111.09 (0.78– 1.52)0.131.99 (1.01– 3.91)0.071.01 (0.38– 2.73)
Child care workers, except private household/4680.180.181.06 (0.85– 1.32)0.130.96 (0.70– 1.30)0.250.84 (0.52– 1.36)0.391.64 (1.06– 2.53)
Farmers, except horticulture/4732.52.91.31 (1.23– 1.39)3.51.30 (1.22– 1.39)1.31.18 (0.95– 1.46)1.51.03 (0.82– 1.30)
Timber cutting and logging occupations/4960.130.151.20 (0.93– 1.54)0.171.32 (1.00– 1.73)0.080.98 (0.43– 2.21)0.090.89 (0.37– 2.16)
Industrial machinery repairers/5180.160.161.00 (0.79– 1.26)0.150.81 (0.61– 1.08)0.131.20 (0.61– 2.34)0.272.28 (1.35– 3.86)
Electrical power installers and repairers/5770.040.061.32 (0.87– 1.99)0.071.56 (1.00– 2.44)0.030.90 (0.22– 3.67)0
Mining machine operators/6160.360.431.27 (1.10– 1.47)0.501.21 (1.03– 1.43)0.311.77 (1.15– 2.73)0.301.33 (0.82– 2.15)
Dressmakers/6660.280.321.22 (1.03– 1.45)0.341.23 (1.01– 1.49)0.251.20 (0.75– 1.92)0.301.32 (0.81– 2.14)
Bakers/6870.100.090.90 (0.66– 1.23)0.080.76 (0.51– 1.12)0.060.67 (0.25– 1.82)0.182.03 (1.07– 3.84)
Typesetters and compositors/7360.030.051.42 (0.90– 2.23)0.041.35 (0.79– 2.32)0.031.02 (0.25– 4.21)0.092.68 (1.08– 6.67)
Miscellaneous textile machine operators/7490.370.421.21 (1.04– 1.40)0.431.17 (0.98– 1.39)0.461.50 (1.05– 2.14)0.431.41 (0.93– 2.12)
Separating, filtering, and clarifying machine operators/7570.050.071.33 (0.91– 1.95)0.081.54 (1.01– 2.34)00.051.05 (0.33– 3.32)
Hand painting, coating, and decorating occupations/7890.020.041.75 (1.04– 2.94)0.031.24 (0.63– 2.43)0.063.73 (1.31– 10.6)0.074.37 (1.57– 12.1)
Supervisors, material moving equipment operators/8430.010.021.82 (0.91– 3.64)0.032.42 (1.20– 4.85)00
Operating engineers/8440.240.291.15 (0.96– 1.37)0.331.26 (1.04– 1.54)0.140.75 (0.40– 1.41)0.271.14 (0.68– 1.91)
Machine feeders and offbearers/8780.100.111.19 (0.90– 1.57)0.101.00 (0.71– 1.42)0.101.15 (0.53– 2.46)0.182.16 (1.14– 4.08)
Garage- and service station–related occupations/8850.040.051.24 (0.81– 1.90)0.030.79 (0.44– 1.44)0.072.26 (0.91– 5.63)0.092.50 (1.01– 6.18)
Homemakers/91435.035.51.01 (0.93– 1.09)37.21.12 (1.01– 1.23)33.00.81 (0.67– 0.96)31.40.83 (0.67– 1.03)
Unemployed/9171.21.21.06 (0.97– 1.16)0.921.20 (1.06– 1.35)2.71.11 (0.95– 1.30)1.30.81 (0.64– 1.03)
Table 3. Associations indicating decreased risk between specific occupations and death from systemic autoimmune diseases*
Occupation/ SIC codeControls (n = 260,632)All cases (n = 52,277)OR (95% CI)RA cases (n = 35,730)OR (95% CI)SLE cases (n = 7,153)OR (95% CI)SSc cases (n = 5,578)OR (95% CI)
  • *

    Except where indicated otherwise, values are the percent. The occupations presented entailed significantly decreased risk (alpha level of 0.05) in at least 1 outcome category (all cases combined, rheumatoid arthritis [RA], systemic lupus erythematosus [SLE], or systemic sclerosis [SSc]) and were represented by at least 5 subjects. SIC = standard industrial classification; OR = odds ratio; 95% CI = 95% confidence interval; NEC = not elsewhere classified.

  • Adjusted for age, sex, race, region, year of death, and socioeconomic status.

Managers and administrators, NEC/193.43.20.93 (0.88–0.98)3.20.92 (0.86–0.98)3.10.95 (0.82–1.09)4.01.09 (0.9–1.25)
Electrical and electronic engineers/550.170.140.79 (0.61–1.01)0.130.73 (0.54–0.99)0.130.96 (0.49–1.88)0.110.63 (0.28–1.41)
Lawyers/1780.140.110.71 (0.53–0.94)0.100.64 (0.45–0.90)0.100.83 (0.39–1.77)0.090.64 (0.27–1.56)
Real estate sales occupations/2540.320.230.71 (0.59–0.86)0.230.74 (0.59–0.93)0.250.78 (0.49–1.25)0.220.58 (0.32–1.02)
Computer operators/3080.110.080.73 (0.53–1.00)0.040.49 (0.28–0.85)0.240.87 (0.53–1.44)0.180.90 (0.48–1.69)
Teachers' aides/3870.090.091.01 (0.74–1.38)0.030.51 (0.28–0.94)0.321.59 (1.02–2.48)0.221.33 (0.74–2.39)
Guards and police, except public service/4260.260.200.73 (0.59–0.90)0.180.72 (0.56–0.93)0.210.62 (0.37–1.05)0.230.75 (0.43–1.31)
Supervisors, food preparation and service occupations/4330.170.100.58 (0.44–0.78)0.100.62 (0.43–0.88)0.110.51 (0.25–1.03)0.160.76 (0.39–1.48)
Waiters and waitresses/4350.760.510.72 (0.64–0.83)0.520.81 (0.70–0.95)0.560.63 (0.46–0.86)0.520.64 (0.44–0.93)
Cooks, except short order/4361.10.910.85 (0.77–0.94)0.800.81 (0.71–0.91)1.20.92 (0.74–1.15)1.10.98 (0.75–1.27)
Nursing aides, orderlies, and attendants/4471.31.00.88 (0.80–0.96)0.780.78 (0.68–0.88)1.91.03 (0.86–1.24)1.40.88 (0.70–1.12)
Maids and housemen/4490.560.430.82 (0.71–0.94)0.330.79 (0.66–0.96)0.840.91 (0.69–1.19)0.410.58 (0.38–0.88)
Attendants, amusement and recreation facilities/4590.060.030.50 (0.29–0.87)0.010.32 (0.13–0.78)0.070.83 (0.34–2.06)0.050.80 (0.25–2.51)
Groundskeepers and gardeners, except farm/4860.140.080.61 (0.44–0.84)0.080.61 (0.41–0.90)0.141.12 (0.59–2.13)0.040.30 (0.07–1.19)
Precious stones and metals workers(jewelers)/6470.050.040.67 (0.41–1.08)0.020.33 (0.15–0.76)0.071.18 (0.47–2.93)0.091.27 (0.52–3.13)
Tailors/6670.050.020.47 (0.26–0.88)0.020.40 (0.19–0.85)0.041.35 (0.43–4.30)0.020.49 (0.07–3.55)
Butchers and meat cutters/6860.150.120.75 (0.57–0.98)0.120.74 (0.54–1.02)0.130.91 (0.46–1.78)0.110.72 (0.32–1.63)
Machine operators, not specified/7790.690.600.84 (0.75–0.95)0.640.93 (0.80–1.06)0.530.72 (0.52–1.00)0.560.71 (0.50–1.02)
Freight, stock, and material handlers, NEC/8830.100.080.70 (0.50–0.98)0.060.59 (0.38–0.91)0.100.84 (0.39–1.80)0.090.86 (0.35–2.10)
Military/9050.510.420.78 (0.67–0.90)0.400.75 (0.63–0.89)0.390.83 (0.57–1.22)0.500.95 (0.65–1.39)
Homemakers/91435.035.51.01 (0.93–1.09)37.21.12 (1.01–1.23)33.00.81 (0.67–0.96)31.40.83 (0.67–1.03)

Farmers (except horticultural) were at increased risk of death from systemic autoimmune diseases (OR 1.3, 95% CI 1.2–1.4). Of the specific diseases, only RA was significantly associated with the farmer occupation, which was also associated with a 30% increased risk of mortality. An analysis of the risks of systemic autoimmune diseases associated with farming-related occupations in either the crop or livestock industry showed that associations with farmer occupation were significant in the crop industry (for RA, OR 1.4, 95% CI 1.3–1.5; for SLE, OR 1.3, 95% CI 1.0–1.6), but not in the livestock industry (for RA, OR 1.1, 95% CI 0.9–1.3; for SLE, OR 0.9, 95% CI 0.5–1.5). For RA, the OR for farmer occupation in the crop industry was significantly different from that for farmer occupation in the livestock industry in a test for multiplicative interaction (P = 0.02).

Several industrial occupations requiring use of machinery or equipment were associated with increased risk of death from systemic autoimmune diseases. Mining machine operators were at increased risk of death from any systemic autoimmune disease (OR 1.3, 95% CI 1.1–1.5), as well as RA (OR 1.2, 95% CI 1.0–1.4) and SLE (OR 1.8, 95% CI 1.2–2.7). Miscellaneous textile machine operators were at increased risk of death from all systemic autoimmune diseases combined (OR 1.2, 95% CI 1.0–1.4) as well as SLE (OR 1.5, 95% CI 1.1–2.1). Separating, filtering, and clarifying machine operators were at increased risk of death from RA (OR 1.5, 95% CI 1.0–2.3). Risk of death from SSc was associated with usual occupation as industrial machinery repairers (OR 2.3, 95% CI 1.4–3.9), typesetters and compositors (OR 2.7, 95% CI 1.1–6.7), and machine feeders and offbearers (OR 2.2, 95% CI 1.1–4.1).

Several other occupations were notably associated with increased risk of death from systemic autoimmune diseases. Timber cutting and logging occupations were associated with risk of death from RA (OR 1.3, 95% CI 1.0–1.7). Firefighters had twice the risk of death from SSc (OR 2.3, 95% CI 1.2–4.5) compared with other occupations. Hand painting, hand coating, and hand decorating occupations were associated with increased risk of death from any systemic autoimmune disease (OR 1.8, 95% CI 1.0–2.9), SLE (OR 3.7, 95% CI 1.3–10.6), and SSc (OR 4.4, 95% CI 1.6–12.1). In addition, artists, performers, and related workers not elsewhere classified were at increased risk of death from SLE (OR 2.9, 95% CI 1.4–6.2). Persons with garage- and service station–related occupations were at increased risk of death from SSc (OR 2.5, 95% CI 1.0–6.2).

Risks of systemic autoimmune disease death associated with occupational exposures assigned by our JEMs are shown in Table 4. Occupational exposure to the public was associated with slightly increased risk of death from SLE (OR 1.1, 95% CI 1.0–1.2). Exposure to animals was associated with increased risk of death from any systemic autoimmune disease, as well as 28% increased risk of death from RA, specifically. Exposure to asbestos was significantly related to death from SSc for any exposure (OR 1.2, 95% CI 1.1–1.3); however, in analyses of exposure intensity, the increased risk was observed with the middle category and not the high category of exposure. Several other occupational exposures were associated with systemic autoimmune disease death for medium- but not high-intensity exposures, including solvents with any systemic autoimmune disease and with RA, radiation with SLE, and lead dust with SSc. For most occupational exposures, relatively few people had high-intensity exposure; thus, exposure-response patterns are unreliable. High-intensity occupational exposure to pesticides was associated with death from any systemic autoimmune disease, as well as with RA. High-intensity exposure to nitrogen oxides was associated with 50% increased risk of death from SSc, with a significant trend with increasing exposure. We also noted inverse associations of high-intensity exposures to silica with any death due to systemic autoimmune diseases (OR 0.7, 95% CI 0.5–1.0) and lead dust with SLE (OR 0.7, 95% CI 0.6–0.9), but neither of these associations showed significant trends with increasing exposure.

Table 4. Associations between JEM exposure intensities and death from autoimmune diseases*
ExposureControls (n = 260,632)All cases (n = 52,277)OR (95% CI)RA cases (n = 35,730)OR (95% CI)SLE cases (n = 7,153)OR (95% CI)SSc cases (n = 5,578)OR (95% CI)
  • *

    Except where indicated otherwise, values are the percent. JEM = job-exposure matrix; OR = odds ratio; 95% CI = 95% confidence interval; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus; SSc = systemic sclerosis; PAHs = polycyclic aromatic hydrocarbons.

  • Adjusted for age, sex, race, region, year of death, and socioeconomic status.

Public
 No (referent)84.984.61.0085.91.0081.21.0082.41.00
 Yes15.115.41.02 (0.99– 1.05)14.11.00 (0.96– 1.03)18.81.10 (1.03– 1.18)17.61.01 (0.94– 1.09)
Animal         
 No (referent)97.296.81.0096.21.0098.21.0098.21.00
 Yes2.83.21.26 (1.19– 1.34)3.81.28 (1.20– 1.36)1.81.14 (0.95– 1.38)1.80.99 (0.80– 1.22)
Asbestos         
 No (referent)85.185.31.0084.91.0087.31.0084.81.00
 Yes14.914.71.02 (0.99– 1.05)15.11.00 (0.96– 1.03)12.71.00 (0.93– 1.08)15.21.16 (1.06– 1.25)
Solvent         
 No (referent)73.273.51.0073.21.0074.41.0074.61.00
 Yes26.826.51.02 (0.99– 1.04)26.81.00 (0.97– 1.04)25.61.06 (0.99– 1.13)25.40.96 (0.90– 1.03)
Benzene         
 No (referent)84.785.11.0084.51.0087.61.0085.61.00
 Yes15.314.90.98 (0.95– 1.01)15.50.97 (0.93– 1.00)12.41.01 (0.93– 1.09)14.41.03 (0.94– 1.12)
Silica         
 No (referent)91.191.51.0091.11.0092.61.0091.81.00
 Yes8.98.50.98 (0.94– 1.02)8.90.99 (0.94– 1.03)7.41.02 (0.92– 1.12)8.21.02 (0.92– 1.13)
Pesticides         
 No (referent)90.090.11.0089.61.0092.11.0091.41.00
 Yes10.09.91.04 (1.00– 1.07)10.41.05 (1.00– 1.09)7.90.96 (0.88– 1.06)8.60.99 (0.90– 1.10)
  Low2.32.20.98 (0.92– 1.04)2.20.97 (0.89– 1.04)1.90.86 (0.73– 1.03)2.61.08 (0.91– 1.28)
  Medium2.42.20.93 (0.87– 0.99)2.30.96 (0.89– 1.03)2.00.92 (0.78– 1.09)2.10.89 (0.74– 1.08)
  High5.35.51.13 (1.08– 1.18)6.01.14 (1.08– 1.20)4.01.07 (0.93– 1.22)3.91.00 (0.86– 1.16)
  P for trend  0.001 0.0003 0.90 0.70
Inorganic dust         
 No (referent)87.988.11.0087.61.0091.01.0088.31.00
 Yes12.112.01.00 (0.97– 1.03)12.41.00 (0.97– 1.04)9.00.91 (0.83– 1.00)11.71.09 (0.99– 1.19)
Lead fumes         
 No (referent)92.292.21.0092.01.0093.61.0092.21.00
 Yes7.87.81.02 (0.98– 1.05)8.01.01 (0.97– 1.06)6.40.96 (0.87– 1.06)7.81.05 (0.95– 1.17)
Lead dust         
 No (referent)87.688.01.0087.91.0090.01.0087.71.00
 Yes12.412.10.97 (0.93– 1.00)12.10.93 (0.89– 0.96)10.00.94 (0.86– 1.03)12.31.07 (0.98– 1.17)
Metal dust         
 No (referent)92.892.71.0092.51.0094.41.0092.81.00
 Yes7.27.31.01 (0.98– 1.05)7.51.01 (0.96– 1.05)5.60.94 (0.85– 1.05)7.21.07 (0.96– 1.19)
Nitrosamines         
 No (referent)96.696.71.0096.51.0097.51.0096.81.00
 Yes3.43.30.97 (0.92– 1.02)3.50.96 (0.91– 1.03)2.50.95 (0.81– 1.11)3.21.06 (0.91– 1.23)
Nitrogen oxides         
 No (referent)92.592.51.0092.11.0094.21.0092.71.00
 Yes7.57.51.01 (0.97– 1.05)7.91.00 (0.95– 1.04)5.81.00 (0.90– 1.12)7.31.10 (0.98– 1.22)
  Low3.73.60.95 (0.86– 1.05)3.70.97 (0.92– 1.04)2.91.18 (0.85– 1.62)3.51.04 (0.89– 1.21)
  Medium3.03.10.98 (0.91– 1.05)3.21.00 (0.94– 1.07)2.31.00 (0.85– 1.16)3.01.07 (0.91– 1.26)
  High0.80.90.98 (0.87– 1.09)1.01.09 (0.97– 1.22)0.61.18 (0.85– 1.62)0.91.52 (1.14– 2.04)
  P for trend  0.20 0.55 0.73 0.03
PAHs         
 No (referent)92.792.61.0092.41.0094.21.0092.81.00
 Yes7.37.41.02 (0.98– 1.06)7.61.00 (0.96– 1.05)5.81.01 (0.90– 1.12)7.21.09 (0.98– 1.22)
Radiation         
 No (referent)85.785.81.0086.51.0085.01.0084.61.00
 Yes14.314.21.00 (0.97– 1.03)13.50.98 (0.94– 1.01)15.01.05 (0.98– 1.12)15.41.04 (0.96– 1.12)
Wood dust         
 No (referent)98.098.01.0098.01.0098.51.0098.01.00
 Yes2.02.01.00 (0.93– 1.07)2.00.99 (0.91– 1.07)1.50.89 (0.73– 1.09)2.01.06 (0.87– 1.29)

Results of the subanalyses were generally consistent with results that included all decedents. Teachers and farmers showed increased risk of death from any systemic autoimmune disease when the subanalysis included only men, women, those who were over 65 years at the time of death, and those whose underlying cause of death was a systemic autoimmune disease. Hand painting, hand coating, and hand decorating occupations also showed similarly elevated risks of death from SLE and SSc in all subanalyses. Results for other occupations were less consistent; for example, the occupation of textile machine operator (miscellaneous) was associated with systemic autoimmune disease deaths only among women. Associations of pesticides with death from RA were elevated among both men and women, but were significant only among men. The association between pesticide exposure and RA was primarily attributable to the risk associated with farmer occupation; the risk associated with pesticide exposure diminished when farmer occupation, which had a low confidence score for pesticide exposure, was excluded from the analysis.

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

In our study, we found that people involved in farming occupations were at increased risk of death from systemic autoimmune diseases, particularly RA, and that farming-related exposures (including animals and pesticides) were also associated with death from systemic autoimmune disease. Several industrial occupations that involve exposures to machinery were associated with increased risk of death from one or more systemic autoimmune diseases, including mining machine operators, miscellaneous textile machine operators, and industrial machinery repairers. Additionally, hand painting, hand coating, and hand decorating occupations were associated with increased risk of SLE and SSc. Exposure to the public was common to many specific occupations (but not all) that were associated with death from systemic autoimmune diseases. Some occupations associated with exposure to the public, particularly food preparation and handling, were associated with reduced risk of death from systemic autoimmune disease.

Using death certificate data on usual lifetime occupation, we attempted to infer occupational risk factors for the development of systemic autoimmune diseases. Use of death certificate data for studies on occupational causes of incident disease has precedent (36–38). Because the job titles used for our analyses were defined as the decedents' “usual” rather than most recent occupations, we intended to capture occupational exposures that occurred prior to the onset of disease. Several studies have shown that although occupations and industries listed on death certificates are not always exact matches with those reported through surrogate interviews, they are fairly reflective of the usual occupation, especially when the decedent had been employed at that occupation for 10 years or longer (39–41). Nevertheless, the data used in our study are limited for inference about occupational causes of incident disease for several reasons. We did not have information on the timing of occupations in relation to disease onset. The degree to which usual occupation reflects a time period prior to disease onset depends on the duration of illness and amount of disability. It is likely that persons with severe autoimmune diseases change to less strenuous jobs as a result of becoming ill. Therefore, because we used death certificate data, it was impossible to distinguish occupational causes of incident autoimmune disease from occupations related to job mobility or death in persons who already had autoimmune disease. Despite these limitations, we sought to generate hypotheses for future studies that would be better suited to describe disease incidence.

The fact that the same occupations and exposures were implicated in our subanalyses restricted to decedents older than the typical retirement age provides some evidence that the occupational exposures were not recent and therefore did not cause rapid death, implying that the occupational exposures were involved in a chronic pathogenic process leading to either disease incidence or slow progression of existing autoimmunity. A related problem is that reporting of usual occupation may lead to the most severely diseased persons who were unable to work not having an occupation listed on their death certificate. However, this problem was minimal, because only 1.4% of our total population had no occupation listed, and this proportion did not differ significantly between cases and controls.

The results of this study suggest that occupational exposure to the public is associated with the risk of death from systemic autoimmune diseases. These results are consistent with a previous analysis of US death certificate data for the years 1985–1995 that detected increased mortality from all rheumatic diseases in teachers compared with persons with other occupations (23), and a study by Bovenzi et al (9) that detected an increased incidence rate of SSc in female teachers; however, an interview-based case–control study found no relationship between the teaching occupation and incidence of SLE (24). Other occupations entailing exposure to the public for which we observed increased risk of death from all systemic autoimmune diseases combined were receptionists and bank tellers. In contrast to this pattern, our data showed that several occupations with large amounts of public contact, such as waiters and waitresses, were associated with decreased risks of death from systemic autoimmune diseases combined and RA, SLE, and SSc. A possible hypothesis for the development of autoimmunity as a result of contact with the public is that exposure to multiple infectious agents leads to an autoimmune response. This theory is supported by evidence that several infectious agents have been linked to autoimmunity (6, 7, 42, 43), but the inconsistency of our results indicates that further studies are needed before firm conclusions can be drawn.

Previous studies have shown associations between farm environments and autoimmune diseases such as RA (44–46). We observed a significantly increased risk of death from RA or any systemic autoimmune disease in farmers. In our analysis of farming-related occupations separated by livestock and crop industries, we found that farmers employed in the crop industry were at increased risk of RA and SLE, whereas farmers employed in the livestock industry were not. Farmers in both industries may be exposed to insecticides; however, crop farmers may be differentially exposed to herbicides, fungicides, solvents, sunlight, silica, and crop dusts. Differentiation between exposures at this level of detail was not possible in our study using JEMs based on occupational title. For example, decedents with farmer occupation (standard occupational classification code 473) were assigned exposure to both animals and pesticides and constituted the majority of persons in these exposure categories. Therefore, the risks associated with specific exposures, independent of other exposures, necessitate a future study with additional information on self-reported tasks and exposure.

Several previous studies found links between solvent exposure and SSc (9, 10, 20, 25–27). We did not detect any association between any solvent or benzene exposure and death from SSc, but we did observe a weak association between solvent exposure and SLE that did not follow an exposure-response trend. Previous studies on the relationship between solvent exposure and systemic autoimmune diseases have yielded mixed results. Lundberg et al (28) found that the handling of organic solvents increased the risk of RA in a cohort of Swedish workers, but De Roos et al (22) found no association between solvents and RA among women in a cohort of pesticide applicators and their spouses. We did observe increased risk of death from systemic autoimmune diseases for several occupations that may entail exposure to solvents, including electrical power installers and repairers, separating, filtering, and clarifying machine operators, industry machinery repairers, mining machine occupations, miscellaneous textile machine operators, and machine feeders and offbearers. Additionally, we found that persons in the hand painting, hand coating, and hand decorating occupations as well as artists and performers, who may be exposed to solvents in paints and paint removers, were at increased risk of SLE and SSc (although estimates were not significant for artists and performers). Nevertheless, all of these occupations typically involve exposures to other agents in addition to solvents.

Several studies have found links between exposure to silica and systemic autoimmune diseases. Previous research that also examined US death certificates from 1982 to 1995 showed associations between silica exposure and RA (16). Additionally, a study in a cohort of silica-exposed industrial sand workers found that, compared with the US population, the cohort had an increased risk of death from RA, with greater risk as the level of silica exposure increased (17). Several studies (8–11) also found associations between exposure to silica and SSc, but many of these associations were weak or imprecise. We did not find any increased risk associated with silica exposure as assigned by our JEM and, in fact, observed an inverse association between high-intensity silica exposure and any systemic autoimmune disease. It is possible that our JEM did not capture important variability in silica exposure between jobs. High silica exposure has been reported for farmers in some regions, particularly those who work in sandy or sandy-loam soils (18, 47). Silica exposure from farming was not incorporated in our JEM and would necessitate assessment by geographic region.

Other research has revealed an association between exposure to asbestos and systemic autoimmune diseases. Noonan et al (48) found that workers in an asbestos-contaminated vermiculite mining company were at twice the risk of either RA, SLE, and SSc compared with an unexposed population. Olsson et al (45) also detected a doubling of the risk of RA in men who were exposed to asbestos. We did not detect an increased risk of death from RA with exposure to asbestos, but we did observe an association between occupational asbestos exposure and SSc.

An increased risk of death from systemic autoimmune diseases in firefighters has not been previously reported. Although few firefighters were included in this study, we determined that they had twice the risk of death from SSc. Firefighters are often exposed to a variety of chemicals during the course of their work, including polycyclic aromatic hydrocarbons, carbon monoxide, nitrogen dioxide, pesticides, and benzene.

Several limitations of our analysis are of note. A drawback of our identification of systemic autoimmune disease cases is that autoimmune diseases tend to be underreported on death certificates. A recent study showed that among SLE patients identified from lupus cohorts, only 60% had SLE listed anywhere on their death certificates (49). That study also showed that decedents without health insurance were significantly less likely to have had SLE listed on their death certificates (49). Such differential recording of autoimmune diseases by health insurance status could have resulted in our underestimating the risk for occupations that are less likely to provide health insurance. Our observation of increased risks in some occupations that often have extensive health benefits even after retirement, such as teachers and firefighters, may be attributable to the fact that persons involved in those occupations had better access to care, rather than to inherent risks involved in their work. Conversely, the inverse associations we observed with occupations such as waiter/waitresses may have been affected by underreporting of existing cases on death certificates due to lower health insurance coverage in this occupation. However, this potential source of bias does not likely explain the associations we saw in occupations that are not associated with lifetime benefits or high SES, such as farming occupations and homemakers. Also, systemic autoimmune diseases may have been more likely to have been reported on death certificates if the diseases were severe, so the associations we observed with autoimmune disease–related death may be truly linked only to severe disease. Another limitation is that we had no information about residential, recreational, or environmental routes of exposure, or confounding factors such as smoking and genetics. However, by matching on age, sex, and race, we were able to account for several of the risk factors for systemic autoimmune diseases.

Using a very large group of decedents that is representative of the US population, we examined associations of usual lifetime occupation and occupational exposures with death from systemic autoimmune diseases. The size of our study allowed us to estimate associations between specific occupations and death from autoimmune diseases and to generate hypotheses that will be useful as starting points for future studies in this area. This study revealed that several occupations and occupational exposures are associated with increased risk of death from systemic autoimmune diseases. Future studies should focus on obtaining more detailed occupational histories from the groups that we found to be at increased risk for one or more systemic autoimmune diseases (e.g., persons employed in farming or industrial occupations and those who work with animals or pesticides). Prospective cohort studies focusing on these occupations would be particularly intriguing.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Ms Gold had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design. Ward, Dosemeci, De Roos.

Acquisition of data. Gold, Ward, Dosemeci, De Roos.

Analysis and interpretation of data. Gold, Ward, Dosemeci, De Roos.

Manuscript preparation. Gold, Ward, Dosemeci, De Roos.

Statistical analysis. Gold, De Roos.

Acknowledgements

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

We gratefully thank Glinda S. Cooper, PhD, for helpful comments on an earlier version of the manuscript.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
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