Associations between disease and occupation Hypotheses generated from the national mortality followback survey.код для вставкиСкачать
American Journal of Industrial Medicine 27:195-205 (1995) Associations Between Disease and Occupation: Hypotheses Generated From the National Mortality Followback Survey Rebecca Reviere, PhD, Scott Schneider, MSIH,and Kathy Woolbright, MA This study uses the National Mortality Followback Survey of 1986 to identify the top five Sentinel Health Events Occupational [SHE(O)s], the five leading causes of death, and to ascertain the primary occupations and industries associated with these. We found that, as expected, cardiovascular diseases were four of the five leading causes of death overall. In addition, the SHE(0) responsible for most deaths was cancer of the trachea, bronchus, and lung, followed by renal failure, bladder cancer, myeloid leukemia, and liver cancer. We employed proportionate mortality ratios to analyze the relationship between industry and occupation and category of mortality. In brief, we validated findings by other researchers; for example, farmers were at lower risk of cancer of the trachea, bronchus, and lung, and workers in eating/ drinking places had excess risk of liver cancer. We also hypothesize other relationships, such as between motor vehicle dealers and bladder cancer. o 1995 Wiley-Liss, Inc. Key words: worker health, mortality, SHEO,cardiovascular disease, cancer, industry, occupation INTRODUCTION While estimates of the frequency of illnesses and deaths due to work conditions vary, their occurrence is sufficient to have attracted the attention of both researchers and practitioners involved with occupational health and safety. A framework for identifying such health problems is the Sentinel Health Events Occupational [SHE(O)]. In 1976, Rutstein et al. developed the original listing of Sentinel Health Events with the assumption that preventable diseases and untimely deaths could be used to monitor the quality of available medical care. The success of this approach with such things as maternal mortality led them in 1983 to apply the method to the field of occupational health with SHE(0)s. This listing was later up-dated by Mullan and Murthy . SHE(0)s are defined as “unnecessary disease, disability, or untimely death which is occupationally related and whose occurrence may: 1) provide the impetus for epidemiologic or industrial hygiene studies, or 2) serve as a warning Department of Sociology/ Anthropology, Howard University, Washington, DC (R.R., K.W.). Center to Protect Workers’ Rights, Washington, DC (S.S.). Address reprint requests to Rebecca Reviere, Department of Sociology/ Anthropology, Howard University, Washington, DC 20059. Accepted for publication March 7, 1994. 0 1995 Wiley-Liss, Inc. 196 Reviere et al. signal that materials substitution, engineering control, personal protection, or medical care may be required” [Rutstein et al., 19831. The purpose of this study was to test whether SHE(0)s are, in fact, validated by occupational data from mortality statistics and to ascertain whether other SHE(0)s could be identified from national mortality data bases. Further, we aimed to identify those occupations and industries whose members are most at risk of the leading causes of death. MATERIALS AND METHODS The data derived from the 1986 National Mortality Followback Survey (NMFS), funded primarily by the National Center for Health Statistics. The NMFS is a nationally representative sample (excluding Oregon) of adults ages 25 or over who died in 1986. The sampling frame was the 1986 Current Mortality Sample, a 10% sample of state death certificates. A total of 18,733 decedents were weighted to a final number of 1,986,868 [U.S. Department of Health and Human Services, 19901. Cause of death was obtained from death certificates and coded according to the Manual of the International Statistical Classification of Diseases, Injuries, and the Cause-ofDeath, Ninth Revision (ICD-9). Occupation and industry codes came from questions regarding ‘‘longest job” and “longest industry” of the decedent. These were categorized according to the ‘‘Classified Index of Industries and Occupations’’ [U.S. Department of the Census, 19801. The information was supplied by informants (usually next-of-kin) who completed questionnaires on the decedent. First, we identified the top five industries and occupations for the five most common causes of death and for the most common SHE(0)s. We then calculated the proportionate mortality ratio (PMR) for each relationship. The PMR compares the observed number of deaths for a particular cause in an occupation or industry group with the expected number of deaths from that cause, based on the proportion of all deaths due to that cause in a standard population [Lalich et al., 19901. RESULTS First, we examined the top five industries and occupations for the five most common causes of death. Tables I through IV show this frequency and the distribution by occupation and industry. Acute myocardial infarction (Table I) was the most common cause of death. Manufacturing (not specified) and construction were the industries with excess risk of this disease. Among individual occupations, we found excess risk among heavy truck drivers, managers and administrators, and supervisors and proprietors/sales workers. The second most common cause of death was ischemic heart disease (Table 11). private households and agriculture were the industries with excess rates of this disorder. Laborers (not in construction) was the occupation with an excess risk. Elementary school teachers also had a slight excess. Cancer of the lung, trachea, and bronchus was the third most common cause of death for workers; this is discussed in the SHE(0) category below (see Table V). The fourth most frequent cause of death in this population was cerebrovascular disease (Table 111). Excesses were observed in the industries of agriculture and department stores. Occupations particularly at risk were sales workers (not elsewhere SHE(0)s in Followback Survey 197 TABLE I. Distribution of Acute Myocardial Infarction (ICD-9=410) as Underlying Cause of Death in the National Mortalitv Followback Survev, 1986* % No. Census code 1060 1010 842 641 392 Census code 473 1019 889 804 243 deaths Sample Populationa PMR Industry Construction Agriculture, crops Elementary, secondary schools Eat/drinking places Manufacturing, not specified 17,829 13,535 9,626 7,849 6,746 6.9 5.2 3.7 3 2.6 6 4.9 4 3.3 2.1 1.15 1.06 0.925 0.91 1.24 Occupation Farmers, not horticulture Managers, administrators Laborers, not construction Heavy truck drivers Supervisors, proprietors/sales 12,719 9,974 8,389 6,447 6,032 4.9 3.9 3.2 2.5 2.3 4.4 3.1 3 2 1.9 1.11 1.26 1.07 1.25 1.21 *Total deaths from this cause, 258,795. "Percentage in this line of work in the total population. TABLE 11. Distribution of Ischemic Heart Disease (ICD-9=414) as Underlying Cause of Death in the National Mortality Followback Survey, 1986* ~ Census code 1010 1060 842 641 76 1 Census code 473 889 313 1019 156 % No. deaths Samde Pouulation" PMR Industry Agriculture, crops Construction Elementary, secondary schools Eat/drinking places Private households 10,672 8,102 7,719 6,107 6,093 5.6 4.3 4.1 3.2 3.2 4.9 6 4 3.3 2.6 1.14 0.12 1.025 0.97 1.23 Occupation Farmers, not horticulture Laborers, not construction Secretaries Managers, administrators, nec Teachers, elementary school 8,241 6,820 5,418 5,191 4,293 4.3 3.6 2.8 2.7 2.3 4.4 3 2.6 3.1 2.1 0.98 1.2 1.08 0.87 1.095 ~_____ *Total deaths from this cause, 190,403. "Percentage in this line of work in the total population. classified [NEC]), supervisors and proprietors/sales workers, and farmers (not horticultural). Other heart diseases were the cause of death occurring fifth in frequency (Table IV). Only minimal excesses were observed by industry. Occupations particularly at risk were cleaners/servants in private households and farmers (not horticultural). Next, we examined the top five industries and occupations for the most common causes of death in the SHE(0) category. Cancer of the trachea, bronchus, and lung was most common, followed by renal failure, cancer of the bladder, myeloid leukemia, and liver cancer. Manufacturing (not specified) and construction industries showed excesses of 198 Reviere et al. TABLE 111. Distribution of Cerebrovascular Disease (ICD-9= 436) as Underlying Cause of Death in the National Mortality Followback Survey, 1986* Census code 1010 1060 842 641 591 Census code 473 274 313 889 243 % No. deaths Sample Population" PMR Industry Agriculture, crops Construction Elementary, secondary schools Eat/drinking places Department stores 5,087 3,411 3,113 2,728 2,470 6.4 4.3 3.9 3.4 3.1 4.9 6 4 3.3 1.4 1.31 0.72 0.975 1.03 2.21 Occupation Farmers, not horticulture Sales workers, nec Secretaries Laborers, not construction Supervisors, proprietorsisales 4,946 3,555 2,169 2,045 1,978 6.2 4.4 2.7 2.6 2.5 4.4 2.5 2.6 3 1.9 1.41 1.76 1.04 0.87 1.32 *Total deaths from this cause, 79,998. aPercentage in this line of work in the total population. TABLE IV. Distribution of Other Heart Disease (ICD-9= 429.2) as an Underlying Cause of Death in the National Mortality Followback Survey, 1986* Census code 1060 1010 761 842 641 Census code 473 407 889 156 313 % No. deaths Sample Population' PMR Industry Construction Agriculture, crops Private households Elementary, secondary schools Eat/drinking places 4,138 3,419 2,636 2,624 2,292 6.1 5.1 3.9 3.9 3.4 6 4.9 2.6 4 3.3 1.02 1.04 1.5 0.975 1.03 Occupation Farmers, not horticulture Private household, cleaners/servants Laborers, not construction Teachers, elementary school Secretaries 3,733 2,194 1,954 1,895 1.400 5.5 3.2 2.9 2.8 2.1 4.4 1.8 3 3.1 2.6 1.25 1.78 0.97 0.9 0.81 ~ *Total deaths from this cause, 67,499. aPercentage in this line of work in the total population. lung cancers (Table V). Occupations particularly at risk were heavy truck drivers, supervisors, and laborers (not construction). Excess risk of renal failure (Table VI) was observed among hospitals, eating and drinking places, and construction workplaces. Cashiers, sales representatives, carpenters, and managers all had excess mortality risk from renal failure. Excess risk of bladder cancer (Table VII) was found among workers in agriculture, hospitals, and motor vehicle dealerships. Occupations especially at risk were sales workers for motor vehicles and boats. Machinists, farmers, secretaries, and managers also had excess rates of this condition. Myeloid leukemia (Table VIII) showed an excess risk for workers in govern- SHE(0)s in Followback Survey 199 TABLE V. Distribution of Cancer of the Trachea, Bronchus, andd Lung (ICD-9 = 162.9) as an Underlying Cause of Death in the National Mortality Followback Survey, 1986* % No. deaths Sample Population" PMR Census code 1060 392 1010 641 842 Industry Construction Manufacturing not specified Agriculture, crops Eat/drinking places Elementary, secondary schools 10,998 4,387 4,145 3,951 3,772 8 3.2 3 2.9 2.7 6 2.1 4.9 3.3 4 1.33 1.52 0.61 0.87 0.67 Census code 889 804 243 1019 47 3 Occupation Laborers, not construction Heavy truck drivers Supervisors, proprietordsales Managers, administrators Farmers, not horticulture 5,577 4,884 3,850 3,934 3,551 4.1 3.6 2.8 2.9 2.6 3 2 1.9 3.1 4.4 1.36 1.8 1.47 0.93 0.59 *Total deaths from this cause, 137,374. "Percentage in this line of work in the total population. TABLE VI. Distribution of Renal Failure (ICD-9= 584) as Underlying Cause of Death in the National Mortality Followback Survey, 1986* Census code 1060 641 831 1010 761 Industry Construction Eat/drinking places Hospitals Agriculture, crops Household cleaners/servants Census code 1019 473 567 276 259 Occupation Managers, administrators Farmers, not horticulture Carpenters Cashiers Sales reps % No. deaths Sample PoDulations" PMR 1,400 954 816 689 537 7.5 5.1 4.4 3.7 2.9 6 3.3 2.4 4.9 2.6 1.25 1.54 1.83 0.73 1.11 5 3.1 4.4 1.3 0.6 0.8 1.61 0.84 2.69 926 696 65 1 622 409 3.7 3.5 3.3 2.2 5.5 2.75 *Total deaths from this cause, 18,639. "Percentage in this line of work in the total population. ment, department stores, and agriculture. Particularly at risk were sewing machine operators. Farm workers, sales workers, and managers were also at excess risk. Excess risk of liver cancer (Table IX) was found in the real estatelinsurance industry, laundry/dry cleaning industry, eating and drinking places, general government workplaces, and construction workplaces. Miscellaneous machine operators showed high risk as did carpenters, secretaries, and laborers (not construction). DISCUSSION The data are subject to several sources of error. First, cause of death comes from the death certificate; we have no way of ascertaining diagnostic accuracy. Second, we 200 Reviere et al. TABLE VII. Distribution of Bladder Cancer (ICD-9= 188) as an Underlying Cause of Death in the National Mortality Sample, 1986* % No. deaths Sample Population” PMR Census code 1010 1060 612 842 831 Industry Agriculture, crops Construction Motor vehicle dealers Elementary, secondary schools Hospitals 1,484 734 634 519 466 10.3 5.1 4.4 4.2 3.2 4.9 6 0.6 4 2.4 2.1 0.85 7.33 Census code 473 1019 313 263 631 Occupation Farmers, not horticulture Managers, administrators Secretaries Sales workers, motor vehicle/boats Machinists 1,278 737 715 427 420 8.8 5.1 4.4 3.1 2.6 0.2 0.9 2 1.65 1.92 15 3.22 5 3 2.9 1.05 1.33 *Total deaths from this cause, 14,445. “Percentage in this line of work in the total population. TABLE MIL Distribution of Myeloid Leukemia (ICD-9= 205) as Underlying Cause of Death in the National Mortalitv Followback Survey, 1986* % No. deaths Sample Population” PMR Census code 1010 591 1060 901 641 Industry Agriculture, crops Department stores Construction General government nec Eat/drinking places 476 412 355 278 269 6.3 5.5 4.7 3.7 3.6 4.9 1.4 6 1.1 3.3 1.29 3.93 0.78 3.36 1.09 Census code 1019 274 477 479 727 Occupation Managers, administrators Sales workers nec Farm supervisors Farm workers Sewing machine operators 417 412 217 217 217 5.5 5.5 2.9 2.9 2.9 3.1 2.5 0 1.3 0.2 1.77 2.2 0 2.23 14.5 *Total deaths from this cause, 7,563. “Percentage in this line of work in the total population. have only longest occupation and industry, as supplied by informants, to indicate work history. Again, we do not know the reliability of these reports. We suspect, however, that using “longest” occupation and industry, even as supplied by informants rather than “usual occupation” as it is coded on the death certificate, should give more reliable results. We understand that these decedents may have changed jobs several times in their lifetimes; however, the average length of the longest job category was well over 5 years, long enough for occupational influences to be felt. Also, because we are examining weighted data, our standard errors are unnaturally low. Further, our choice of analysis is the PMR. The PMR in itself can be a source of error because it uses the population of decedents for the denominator instead of the SHE(0)s in Followback Survey 201 TABLE IX. Distribution of Liver Cancer (ICD-9= 155) as Underlying Cause of Death in the National Mortality Followback Survey, 1986* Census code 1060 641 712 77 1 90 1 Census code 777 567 889 869 313 % No. deaths Samule Pouulation" PMR Industry Construction Eatldrinking places Real estatehsurance Laundrykleaning General government nec 589 555 404 272 27 1 8.3 7.8 5.7 3.8 3.8 6 3.3 0.9 0.8 1.1 1.38 2.36 6.33 4.75 3.45 Occupation Miscellaneous machine operators Carpenters Laborers, not construction Construction workers Secretaries 432 402 333 319 317 6.1 5.7 4.7 4.5 4.5 0.9 1.3 3 1.5 2.6 6.78 4.38 1.57 3 1.73 *Total deaths from this cause, 7,115. "Percentage in this line of work in the total population. population-at-risk; this can result in the problems of overstating or understating risk [DecouflC et al., 19801. In addition, common causes of death may be over and underrepresented [Milham, 19751. It is, however, considered useful for exploratory, hypothesis-generation research [Dubrow et al., 19871 which matches our purpose here, i.e., to investigate possible relationships that may exist for use in further study. Four of the top five causes of non-SHE(0) death in this study were cardiovascular diseases (CVD) as expected, and we discuss these as a group. A great deal of research has investigated links between coronary outcomes and occupational factors. These include specific hazards, such as carbon disulfide and nitroglycerin exposure [Kristensen, 1989bl and nonspecific job stressors including work overload [House, 19751, shift work, low control [Alfredsson and Theorell, 19831, sedentary occupation [Kannel and Sorlie, 1979; Kannel et al., 19861, noise [Kristensen, 1989a], and status [Buring et al., 1987; Dobson et al., 19851. Groups with elevated PMRs for CVD in this study were managerdadministrators, heavy truck drivers, supervisors/proprietors in sales, cleaners and servants in private households, sales workers, fanners, and department store workers. These findings make sense theoretically, fitting within the framework of previous findings. Cleaners and servants are among the lowest status workers; sales workers could be expected to have high job strain due to pressure for commissions. Again, job strain may explain why managers/administrators have relatively high PMRs for CVD. Heavy truck drivers have a relatively inactive occupation, likely combined with high rates of cigarette smoking and exposure to carbon monoxide, which is associated with heart attacks. Manufacturing workers may use chemicals such as methylene chloride which can also increase risk of heart attack. The third most common cause of death and most frequent SHE(0) in our sample, as in other research [Feldman and Gerber, 19901, was cancer of the trachea, bronchus, and lung. We found, as have others [Dubrow and Wegman, 1984; Ng, 1988; Lynge and Thygsen, 1990a1, that construction and manufacturing industries had relatively high rates, possibly through asbestos exposure [Glass et al., 19911. In 202 Reviere et al. addition, associations between driving heavy trucks and this disease have been found [Dubrow and Wegman, 1982; Ng, 1988; Zahm et al., 19891;exposure to diesel fumes and motor exhaust [Hayes et al., 19891 may be implicated. Our findings that those working in farming had relatively low risk supports the work of others [Zahm et al., 1989; Lynge and Thygsen, 1990a; Frey and Glenn, 19881. It is difficult to interprete these results without knowledge of cigarette smoking, however. Renal failure was the second most frequent SHE(0). It has been associated with exposure to cadmium [Brown et al., 19871, lead [Wedeen, 1983; Lurakis and Pitone, 1984; Van de Vyver et al., 19881, phenol [Foxall et al., 19891, organophosphates [Bardin et al., 19871, and solvents, such as carbon tetrachloride [Muehrcke et al., 19761. We observed excesses among workers in hospitals, eating and drinking establishments, and construction sites. Cashiers, carpenters, sales representatives, managers, and administrators also had a high relative risk of renal failure. Construction workers, including carpenters, can have exposure to solvents which have renal toxicity, as can hospital workers. Lead is also a common exposure among construction workers doing renovation work on houses or bridges covered with lead paint. Sales representatives, cashiers, managers, and administrators generally do not have these types of exposures but could experience job stress which is associated with high blood pressure, a possible contributor to renal damage. Excess risk of bladder cancer was found among agricultural workers and farmers, for which there were no proven explanations in the literature. However, La Vecchia et al. [I9901 reported positive trends for exposure to herbicides, but in general findings are mixed [Blair et al., 1985; Une et al., 1987; Burkart et al., 1978; Bross et al. , 19781. Unexplained risks were also found for secretaries, hospital employees, and motor vehicle dealers. Motor vehicle and boat sales workers were at high risk for bladder cancer, results which might be explained by exposure to exhaust and gases [La Vecchia et al., 1990; Silverman et al., 19861. Other researchers have also found excesses in machinists and related workers and attribute these to the aromatic amines commonly used as antioxidants in cutting oils [Milham, 1976; Dubrow and Wegman, 1982; Vineis and Prima, 1983; Vineis and Magnani, 19851. Managers were also found to have excesses of bladder cancer, a finding supported by other researchers [Gonzalez et al., 19891. Myeloid leukemia was the fourth most frequent SHE(0). It has been associated with several exposures, including low frequency radiation from electrical fields [Juutilainen et al., 19901, benzene [Lamm et al., 1989; Lumley et al., 19901, and ethylene oxide [Hogstedt et al., 19791. Our data showed higher relative risks of myeloid leukemia in department store workers, government workers (NEC), agricultural crop workers, and the occupations of sewing machine operators, farm workers, sales workers (NEC), and managers and administrators. The industries and occupations at higher risk generally do not have exposures to known risk factors. The only possible connection is pesticide exposures of farm workers and potential solvent exposures among sewing machine operators. Many of these associations are unexplained by occupation and may be due to small sample sizes. The fifth SHE(0) in this study was liver cancer. Consistent with other researchers [Lynge and Thygsen, 1990a; Suarez et al., 1989; Ng, 19881, we found that construction workers had high rates for liver cancer. Other studies have found that, as in our study, cooks, bar workers, and chefs [Dubrow and Wegman, 1984; Suarez SHE(0)s in Followback Survey 203 et al., 19891 have increased risk. These workers likely have high rates of alcohol and cigarette use which may contribute to the disorder [Dubrow and Wegman, 19841. Our finding that those in the laundry and cleaning industry were at risk replicates other findings [Nakamura, 1985; Lynge and Thygsen, 1990bl; it has been suggested that in this environment, tetrachloroethylene exposure increases risk. On the other hand, McLaughlin et al. [ 19871 did not find an increased incidence of liver cancer among laundry and dry cleaning workers. CONCLUSIONS In conclusion, the occupational origins of many excess risks seen here are unclear. SHE(0)s are generally discovered because of high rates of a rare disease among small populations of exposed workers. Our study looked at the most common causes of death and, consequently, did not prove an effective means of identifying new SHE(0)s. Many existing associations were validated, however. The unusually high excesses found (i.e., motor vehicle dealers and bladder cancer) suggest hypotheses which deserve further exploration, although the magnitude of excess may be attributable to small sample size. In addition, as we investigated mortality, conditions may be present but not a primary cause of death; they would, therefore, be undetected here. While the connection with occupation may seem obscure now, perhaps this method can help find SHE(0)s which have not been readily apparent. In the long run, hypotheses and statistics generated should be applied to improve work conditions and worker health and safety. To this end, improvements in data collection would be useful [Feldman and Gerber, 19901. Future studies might focus on possible links between conditions which are less directly associated with disease and on intervention approaches which lessen likelihood of illness. At the same time, the implication of worker health habits, such as smoking and diet, and their possible additive or multiplicative effect with unhealthy work conditions should be considered. ACKNOWLEDGMENTS This research was funded, in part, by a New Faculty Research Grant (19205) from Howard University. REFERENCES Alfredsson L, Theorell T (1983): Job characteristics of occupations and myocardial infarction risk: Effect of possible confounding factors. Soc Sci Med 17:1497-1503. Bardin PG,Van Eeden SF, Joubert JR (1987): Intensive care management of acute organophosphate poisoning: A 7-year experience in the Western Cape. S Afr Med J 72593-597. 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