OCCUPATIONAL EXPOSURES AND ABNORMAL PROSTATE CANCER SCREENING RESULTS IN BLACK AND WHITE MEN

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Abstract: Little is known about possible associations between occupational exposure and abnormal prostate cancer screening results, especially in black men. This research investigated associations between occupational exposure and abnormal prostate cancer screening results (digital rectal examination [DRE] an d prostate specific antigen [PSA]) in a cohort of 943 community-based men of which 65% were black. Data on occupational exposures was collected via a telephone interview. Exposure to pesticides and motor oil, gasoline, turpentine, or paints as well as 10 different occupations including the tire/rubber industry and the battery (cadmium) industry was collected. The highest prevalence of occupational exposure was the textile industry with 28% of black and 51% of white men having worked in this industry. Tire/rubber industry was significant for abnormal PSA for white men (OR = 5.14, 95% CI, 1.19-22. 16).

Prostate cancer is the most common site of major cancer among men. In the United States, the American Cancer Society projects that it will be diagnosed in 179,300 men in 1999 and over 37,000 will die from prostate cancer (American Cancer Society, 1999). There is large international variation in prostate cancer rates and this has led to the theory that environmental and lifestyle factors account for differences in incidence. Within the United States, there is large variation in prostate cancer rates according to racial/ethnic groups, with black men experiencing the highest incidence (180.6 per 100,000 men) and mortality rates (53.7 per 100,000 men) (Kosary et al., 1995). Not only is their incidence of prostate cancer about 50% higher than among white men, blacks are diagnosed at younger ages, with more advanced stages of cancer, and they experience a poorer 5-year survival rate (Wingo et al., 1996). Recent improvements in prostate cancer detection in earlier stages and survival rates have not benefitted African American men to the extent that they have Caucasian men. (Mettlin, Murphy, McGinnis, & Menck, 1995). The reasons for these differences are poorly understood (Eyre & Feldman, 1998). The causes of prostate cancer are virtually unknown other than for established demographic risk factors which can not be altered of age, race (African American), and family history, (American Cancer Society, 1999; Carter et al., 1993; Haas & Sakr, 1997). There is conflicting data on the risk factors of occupational history, income, sexual history, vasectomy, smoking, vitamin D, body mass index, and physical inactivity (Giovannucci, Rimm, Stampfer, Colditz, & Willett, 1997; Rodriguez, Tatham, Thun, Calle, & Heath, 1997; Whittemore et al., 1995).

Most of the literature on occupational risk factors is related to prostate cancer. There is minimal data on occupational risk factors and abnormal screening results. Repairmen, mechanics, and workers exposed to cadmium, metal, rubber, and tire manufacturing have been linked with increased prostate cancer risk (Elghany, Schumacher, Slattery, West, & Lee, 1990); Sanchez, Antona, & Urrutia, 1992; Waalkes & Rehm, 1994; Van der Gulden, 1997; Fincham, Hilt, Hanson, & Wiljayasinghe, 1990; Hagmar et al., 1991). A nested case-control study within a cohort of rubber workers showed increased risk associated with exposure to heavy metal oxides (Goldsmith, Smith, & McMichael, 1980). There is conflicting data on the risk associated with farming with several studies reporting an increased risk (Van der Gulden, 1997; Hagmar et al., 1991; Morrison et al., 1993; Blair, Dosemeci, & Heineman, 1993), and one study in Utah reporting no increased risk (Elghany et al., 1990). A recent study estimates that the population attributable risk for prostate cancer and several occupational exposures may be between about 10 and 20%, representing an important public health issue (Aronson, Sierniatycki, Dewar, & Gerin, 1996). However, few occupational studies have included the men with the highest prostate cancer incidence and mortality rates: African American men.

The American Cancer Society recommends men be informed of both the risks and benefits of prostate cancer screening (von Eschenbach, Ho, Murphy, Cunningham, & Lins, 1997). Annual screenings beginning at age 50 are recommended by the American Cancer Society (von Eschenbach et al., 1997), the American Urological Association (1992), and the American College of Radiology (1991). Black men are encouraged to start at younger ages. In contrast, the United States Preventive Task Force and the National Cancer Institute do not recommend PSA for prostate cancer screening (Regional Cancer Institute Cancer Control Objectives for the Nation 1986, US Preventive Services Task Force, 1996). Men who have their prostate cancer detected in the early stages have 100% five year survival rates, versus only 30% for men who have their cancer diagnosed in advanced stages (ACS, 1999). Prostate cancer screening includes both a prostate specific antigen (PSA) and digital rectal examination (DRE). An elevated PSA test does not differentiate between prostate cancer and other prostate diseases such as benign prostatic hyperplasia (BPH). An elevated PSA is an indicator of prostate cancer risk. However, prostate cancer is diagnosed with a biopsy. It is important to investigate possible associations between occupational exposures and abnormal prostate cancer screening results for clues to pre-disease risk factors. This research reports on prostate cancer screening results among 943 men, 65% of which are black, who participated in a larger study designed to educate men about prostate cancer screening (Weinrich SP, Weinrich MC, Boyd, Mettlin, 1998).

METHODS
Study Subjects. Men were recruited from community sites in 11 counties in central South Carolina that included work sites, churches, housing projects, National Association for Advancement of Colored People (NAACP) sites, and barber shops (Weinrich SP, Boyd, Greene, Mossa, & Weinrich MC, 1998). Inclusion criteria were age of 40 - 70 years old for black men and age of 50 - 70 years for white men, no history of prostate cancer, not currently undergoing diagnostic evaluation for prostate cancer, and informed consent. Data is not available on the number of men who were at the sites, but failed to attend the educational program at the sites.

Data Collection and Analyses. Data was collected at three intervals: (1) demographics, family history of prostate cancer, and urinary symptoms were collected in person at the initial interview; (2) screening results from physicians and laboratory companies were obtained following the prostate cancer screening; and (3) occupational exposure and smoking history were obtained one year after the prostate cancer screening via telephone. During the initial interview, each man completed a 20 minute questionnaire after signing an informed consent. After completion of the questionnaire, all men saw a slide-tape presentation on prostate cancer which included statistics, risk factors, symptoms, and screening for prostate cancer. It carried a positive message of, "If you don't want to do it for yourself, do it for the ones you love." Each man was referred to his personal physician for prostate cancer screening that included a digital rectal examination and a prostate specific antigen (PSA) test. Financial help for additional diagnostic tests (i.e. ultrasound and biopsy) was obtained for low-income men who could not afford it. One year after the prostate cancer screening was completed, each man was called and asked a series of questions by telephone, including the occupational exposure questions.

The 44-item questionnaire in the initial interview had a fifth-grade readability level to aid in comprehension. Data that measured occupational exposure, demographics, symptoms, family history of prostate cancer, smoking, and previous history of prostate cancer screening was reported here. The occupational questions were designed by the researchers following a comprehensive review of the literature. Three major questions on occupational exposure were: (1) Have you ever been exposed to chemicals that kill pests, insects, or plants?; (2) Have you ever been exposed to substances such as motor oil, gasoline, turpentine, or paints for more than one hour at a time?; (3) Have you ever worked in any of the following industries? For this analysis, occupations recorded were: tire/rubber industry, battery industry, janitorial/cleaning industry, textile industry, mining, grounds maintenance, tobacco industry, medicine/science, manufacturing food products, paper/ wood industry, and entertainment/recreation.

The six questions that recorded demographics (age, ethnicity, education, income, marital status, and living status) were adapted from an earlier study by the authors (Weinrich, Weinrich, Stromborg, Boyd, & Weiss, 1993). Five questions measured previous CaP screening: recipient's knowledge of digital rectal examination (DRE); receipt of DRE in lifetime and if "yes", receipt of DRE in last year; receipt of prostate specific antigen (PSA), and if "yes", receipt of PSA in last year. The three DRE questions were modified from the National Health Interview Survey (Center for Disease Control, 1995) and the two PSA questions were developed by the researcher following the same format as the DRE questions. The questions were asked before an educational program on CaP screening was given.

The results of the CaP screening were obtained from the DRE forms returned by the physicians' offices and the PSA forms from the laboratories. The three options for evaluating DRE: normal, BPH, abnormal and explain, were modified from the American Cancer Society National Prostate Cancer Detection Program (ACS-NPCDP) (Mettlin et al., 1996). The majority of the PSAs (80.6%) were performed by SmithKline which used a hybritech technique; 9.6% were performed by Labcorp using a hybritech technique and the remaining 9.8% were performed by other laboratory companies. A PSA greater than 4.0 ng/ml was considered abnormal.

Statistical Analyses. General distributions and prevalence of occupational exposures, demographic, previous urinary symptoms, family history of prostate cancer, lifetime smoking status, abnormal screening and prostate cancer were completed by race groups (black and white) for the 943 men. There was an insufficient number of men exposed to mining in South Carolina and mining exposure was deleted from all the analyses. Appropriate chi-square or t-test analyses were done on all possible factors to determine differences between the two racial groups. Significant differences by race were found, and the remaining analyses were stratified by race.

Univariate logistic regression analyses were conducted to determine possible associations with abnormal DRE and abnormal PSA. In building multivariate models, all modeling was done separately for the two race groups. All models presented were adjusted for age, income, education, marital status, and family history of prostate cancer. In addition to these variables, models for black men were also adjusted for urinary symptoms, and models for white men were adjusted for previous blood test for prostate cancer and whether they had ever been a smoker. Models for exposure to chemical and oil products were also controlled for each of the ten occupations examined. Each final model included all variables that satisfied either of two criteria for inclusion: (1) showing a statistically significant association with the outcome (p<0.05), or (2) showing possible confounding by producing a 10% or greater change in the regression coefficient according to whether the variable was included in or dropped from the model.

RESULTS
Sample description. Table 1 shows the distribution of the 943 men screened. Sixty five percent of the men were black with 305 of the men between the ages of 40-49 years old. Comparing men within the same age range of 50-70 years old, (n=310 blacks and 328 whites), blacks had significantly lower education and household income, fewer were married, and fewer had had a previous DRE in their lifetime. Other variables were approximately the same in the two groups. Overall, 26 to 30% reported urinary symptoms and pain, 14% reported a family history of prostate cancer, and 62% of the men had smoked (Table 1).

Among the 615 black men screened for prostate cancer, 564 had normal screens (n=272 for ages 50-70) as did 300 of the 328 whites screened. Considering only those 50-70 years, 12.3% of blacks and 8.6% of whites had an abnormal DRE and/or PSA screening result. Among men with abnormal results who were 50-70 years old, 35% had abnormal DREs only. Seven percent of black 50-70 year-old men had abnormal PSAs in contrast to 4.6% whites (Table 1). The percent of abnormal DRE and PSA was similar (1.0% in blacks 50-70 year old and 0.6% in whites (Table 1).

The highest prevalence of occupational exposure was the textile industry with 28% of black and 51% of white men having worked in this industry (Table 2). No difference between blacks and whites was seen in prevalence of work in the following occupations: tire/rubber workers, paper/wood workers, entertainment, tobacco workers, and medicine. Whites had statistically significant higher prevalence of exposure to pesticides, motor oil, gas, etc. and work in textile and battery industries. In contrast, blacks had statistically significant higher prevalence of work in the following jobs: janitorial/cleaning, grounds maintenance, and manufacturing foods (Table 2).

Associations with Abnormal Screening Results. Univariate logistic regression results for each of the two outcomes, abnormal DRE and abnormal PSA, by race are presented in Table 3. Odds ratios and 95% confidence intervals (CI) are shown. A variable is considered to be statistically significant if the 95% CI does not contain the null value of I for the odds ratio. Those variables which showed significant odds ratios are shown in bold. While not statistically significant, the odds ratios for the relationship between pesticide exposure and abnormal DRE (OR=2.33, 95% CI, 0.89-6.10) and abnormal PSA (OR=1.82, 95% CI, 0.72-4.61) are slightly elevated for blacks. There is also an elevation in the odds ratio for the relationship between exposure to oil products and abnormal DRE as well as abnormal PSA in blacks, but the association is not statistically significant.

Significant associations in the univariate analysis were age, education, marital status, urinary symptoms, and pain (Table 3). Age was significantly associated with abnormal PSA screening outcomes among both race groups and with abnormal DRE screening outcomes among black men. Less than a 9th grade education was associated with an abnormal PSA among black men (OR = 6.91, 95% CI, 3.24-14.77). In black men, the presence of urinary symptoms was statistically related to abnormal PSA (OR = 3.04, 95% CI, 1.44-6.41), and pain was statistically related to abnormal PSA (OR = 2.69, 95% CI, 1.28-5.63) in black men.

Final multivariate logistic regression models by race groups for each outcome of abnormal DRE and abnormal PSA were found and are presented in Table 4. Models were constructed for the two main exposures, oil products and chemical products, and for each of the occupations. No statistically significant associations were observed between the major independent variables and abnormal screening results for either race group. However, for blacks, the odds ratios for the associations between exposure to pesticides as well as exposure to oil products, gasoline, turpentine products, or paints were elevated with both abnormal DRE and abnormal PSA. Final models for whites for abnormal PSA and chemical products, and abnormal PSA and oil products could not be determined due to a small sample size.

There were no significant associations between occupational exposures and abnormal screening results in the univariate analyses (Table 3). However, in the multivariate analyses, tire/rubber industry was significant for abnormal PSA for white men (OR = 5.14, 95% CI, 1.19-22.16) (Table 4). There were no other significant occupational exposures. There were too few white men with abnormal PSA results and exposure to several occupations to measure the association.

DISCUSSION
This study investigated the relationship between occupational exposure and prostate cancer screening results in a sample of community-based blacks and whites. The only significant occupational exposure was abnormal PSA results with the tire/rubber industry for white men. The literature lacks data on the tire/rubber industry and PSA values. And there is conflicting data on the association between tire/rubber industry and prostate cancer (Bosland, 1988). An increase in prostate cancer mortality rates was found in 6,678 male rubber workers (McMichael, Spirtas, Gamble, & Tousey, 1976). Bernadelli, de Marco, & Tinelli (1987) reported increased risk in an Italian plant based on two cases of prostate cancer. In contrast, Elghany et al. (1990) had no association with rubber and prostate cancer in 358 white cases and 679 white controls from Utah, and Hakama and Kilpikairi (1980) found no association in a summary of four studies on 80,966 men with 204 cases. Interestingly, Sorahan, Parkes, Veys, & Waterhouse (1986) found a statistically significant decrease in risk in rubber Workers. Larger studies need to further investigate this issue which could identify high risk occupational groups. Specific job categories need to be measured in future groups as increased risk was found for workers in batch preparation/mixing and calendering divisions in two case-control studies (McMichael et al., 1976; Goldsmith et al., 1980). It is premature to recommend increased surveillance of men in tire/rubber occupations. However, if the association between occupations of tire or rubber and abnormal PSA values is supported in future cohorts, men who work in tire or rubber industries will need to be screened at earlier ages and/or more often. There were no significant occupational exposures for black men although pesticide exposure as well as exposure to oil products revealed strong associations. This cohort of black men also needs to be followed over time to identify if these associations are related to future development of prostate cancer. If so, similar increased screening could be indicated in men exposed to pesticides, oil products, and paints.

This study was conducted in 11 counties in central South Carolina. Caution should be used in generalizing results to other geographic areas. Interpretation of data needs to consider that this study was not designed explicitly to test etiologic hypotheses. Uncertainties associated with retrospective data apply to this study which measured occupational exposure six months to one year after the men who participated in the free prostate cancer screening knew the results of their screening. A strength of this study is its description of prostate cancer screening results among under-served men who have the highest prostate cancer mortality rates.

Table 1 Description of Men Screened in Prostate Cancer Project
Legend for chart:

A1=Variables
A2=Demographics
A3=Age (Mean (SD))
A4=40 - 49 years
A5=___________
A6=___________
A7=50 - 70 years
A8=___________
A9=Household Income[sup*]
B1=Less than $9,600
B2=$9,601- $25,020
B3=Greater than $25,021
B4=Education[sup*]
B5=Grades 0-8
B6=Marital Status[sup*]
B7=Not Married
B8=Urinary symptoms
B9=Pain in lower back,
groin, upper part of legs,
testicles, and rectum
C3=Family History of CaP
C4=Smoking (Ever)
C5=Previous History of Screening
C6=Previous DRE in lifetime[sup*]
C7=Previous PSA in lifetime
C8=Screening Results
C9=Normal Screen
D1=Mean PSA (SD)
D2=Abnormal DRE only
D3=Abnormal PSA only.
D4=Abnormal DRE and PSA
A1 Black Men White Men Total

Age 40 - 49 Age 50 - 70 Age 50 - 70 N=943
N=305 N=310 N=328 N (%)
N (%) N (%) N (%)

A2
A3 44.55(2.75) 57.13(5.50) 57.34(5.28) 53.13 (7.57)
A4 305 (100.0) A5 A6 305 (32.3)
A7 A8 310 (100.0) 328 (100.0) 638 (67.7)
A9
B1 41 (13.4) 101 (32.6) 26 ( 7.9) 168 (17.8)
B2 136 (44.6) 120 (38.7) 114 (34.8) 370 (39.2)
B3 128 (42.0) 89 (28.7) 188 (57.3) 405 (43.0)
B4
B5 16 ( 5.2) 73 (23.5) 36 (11.0) 125 (13.3)
Grades 9+ 289 (94.8) 237 (76.5) 292 (89.0) 818 (86.7)
B6
B7 61 (20.0) 62 (20.0) 34 (10.4) 157 (16.6)
Married 244 (80.0) 248 (80.0) 294 (89.6) 786 (83.4)
Symptoms
B8 64 (21.0) 83 (26.8) 102 (31.1) 249 (26.4)
B9 97 (31.8) 91 (29.4) 94 (28.7) 282 (29.9)
C3 35 (11.5) 41 (13.2) 59 (18.0) 135 (14.3)
C4 172 (56.4) 204 (65.8) 212 (64.6) 588 (62.4)
C5
C6 184 (60.3) 224 (72.3) 271 (82.6) 679 (72.0)
C7 47 (15.4) 94 (30.3) 122 (37.2) 263 (27.9)
C8
C9 292 (95.7) 272 (87.7) 300 (91.4) 864 (91.4)
D1 0.91(0.94) 2.11(4.82) 1.95(9.88) 1.67(6.52)
Abnormal
D2 9 (3.0) 12 (3.9) 11 (3.4) 32 (3.4)
D3 4 (1.3) 23 (7.4) 15 (4.6) 42 (4.4)
D4 0 (0.0) 3 (1.0) 2 (0.6) 5 (0.5)
CaP 3 (1.0) 12 (3.9) 8 (2.4) 23 (2.4)

[sup*] Significant differences by race comparing 50 - 70
y. o. AAM to 50 - 70 y. o. CM (p<0.05)
Table 2 Frequency of Occupational Exposure in Black and White Men
Legend for chart:

A1=Exposure
A2=Black Men, 40-70 y.o.
A3=Yes
A4=No
A5=N (%)
A6=N (%)
A7=Chemicals that kill pests,
A8=77 (12.5)
A9=538 (87.5)
B1=insects, or plants
B2=Daily
B3=14 (2.3)
B4=Weekly
B5=7 (1.1)
B6=Monthly
B7=56 (9.1)
B8=Motor oil, gasoline, turpentine,
B9=73 (11.9)
C1=542 (88.1)
C2=turpentine, or paints for more
C3=than one hour at a time
C4=Daily
C5=25 (4.1)
C6=Weekly
C7=7 (1.1)
C8=Monthly
C9=41 (6.7)
D1=Occupations
D2=Tire/Rubber Industry
D3=44 (7.2)
D4=571 (92.8)
D5=Battery (Cadmium) Industry
D6=22 (3.6)
D7=593 (96.4)
D8=Janitorial/Cleaning Industry
D9=158(25.7)
E1=457 (74.3)
E2=Grounds Maintenance
E3=119(19.4)
E4=496 (80.6)
E5=Manufacturing Food Products
E6=41 (6.7)
E7=574 (93.3)
E8=Paper/Wood Industry
E9=114 (18.5)
F1=501 (81.5)
F2=Entertainment/Recreation
F3=32 (5.2)
F4=583 (94.8)
F5=Tobacco Industry
F6=35 (5.7)
F7=580 (94.3)
F8=Medicine/Science
F9=26 (4.2)
G1=589 (95.8)
G2=Textile Industry
G3=169 (27.5)
G4=446 (72.5)
A1 A2 White Men, 50-70 y.o. Total

A3 A4 Yes No Yes No
A5 A6 N (%) N (%) N (%) N (%)

A7 A8 A9 72 (22.0) 256 (78.0) 149 (15.8) 794 (84.2)
B1
B2 B3 16 (4.9) 30 (3.2)
B4 B5 7 (2.1) 14 (1.5)
B6 B7 49 (15.0) 105 (11.1)
B8 B9 C1 52 (15.9) 276 (84.1) 125 (13.3) 818 (86.7)
C2
C3
C4 C5 21 (6.4) 46 (4.9)
C6 C7 11 (3.4) 18 (1.9)
C8 C9 20 (6.1) 61 (6.5)
D1
D2 D3 D4 26 (7.9) 302 (92.1) 70 (7.4) 873 (92.6)
D5 D6 D7 23 (7.0) 305 (93.0) 45 (4.8) 898 (95.2)
D8 D9 E1 22 (6.7) 306 (93.3) 180 (19.1) 763 (80.9)
E2 E3 E4 31 (9.5) 297 (90.5) 150 (15.9) 793 (84.1)
E5 E6 E7 8 (2.4) 320 (97.6) 49 (5.2) 894 (94.8)
E8 E9 F1 40 (12.2) 288 (87.8) 154 (16.3) 789 (83.7)
F2 F3 F4 14 (4.3) 314 (95.7) 46 (4.9) 897 (95.1)
F5 F6 F7 11 (3.4) 317 (96.6) 46 (4.9) 897 (95.1)
F8 F9 G1 7 (2.1) 321 (97.9) 33 (3.5) 910 (96.5)
G2 G3 G4 167 (50.9) 161 (49.1) 336 (35.6) 607 (64.4)
Table 3 Univariate Logistic Regression of Occupation Exposure on Abnormal Screening Findings
Legend for chart:

A1=Variables
A2=Abnormal DRE
A3=Black
A4=(n=24)
A5=OR (95% CI)
A6=Exposure
A7=Pesticides
A8=2.33 (0.89, 6.10)
A9=Motor oil, gasoline,
B1=1.91 (0.69, 5.31)
B2=____________
B3=turpentine, or paints
B4=Occupations
B5=Tire/Rubber Industry
B6=0.51 (0.07, 3.87)
B7=____________
B8=Battery Industry
B9=____________
C1=Janitorial/Cleaning
C2=1.82 (0.78, 4.26)
C3=____________
C4=Industry
C5=Grounds Maintenance
C6=1.41 (0.54, 3.64)
C7=____________
C8=Manufacturing Food
C9=1.20 (0.27, 5.30)
D1=____________
D2=____________
D3=____________
D4=Products
D5=Paper/Wood Industry
D6=1.19 (0.43, 3.27)
D7=Entertainment/Recreation
D8=____________
D9=4.55 (0.90, 23.03)
E1=____________
E2=Tobacco Industry
E3=0.73 (0.10, 5.58)
E4=____________
E5=____________
E6=Medicine/Science
E7=____________
E8=____________
E9=Textile Industry
F1=0.88 (0.34, 2.27)
F2=Demographics
F3=Age
F4=1.06 (1.01, 1.11)[sup*]
F5=1.13(1.07, 1.18)[sup*]
F6=1.17 (1.06, 1.28)[sup*]
F7=Income
F8=< $9,600/year
F9=4.34 (0.54, 34.81)
G1=1.22 (0.11, 14.09)
G2=4.32 (0.97, 19.31)
G3=> $25,020/ year
G4=1.91 (0.24, 14.90)
G5=Less than 9th Grade
G6=0.54 (0.12, 2.33)
G7=6.91 (3.24, 14.77)[sup*]
G8=____________
G9=Education
H1=Not Married
H2=2.38 (1.01, 5.59)[sup*]
H3=Symptoms
H4=Urinary
H5=2.01 (0.86, 4.72)
H6=3.04 (1.44, 6.41)[sup*]
H7=Pain in back, groin,
H8=0.91 (0.37, 2.23)
H9=2.69 (1.28, 5.63)[sup*]
I1=legs, testicles
I2=Family History of
I3=1.51 (0.50, 4.57)
I4=Prostate Cancer
I5=Smoking (Ever)
I6=0.65 (0.27, 1.60)
I7=Previous History of Screening
I8=Ever Had Rectal Check-Up
I9=1.32 (0.54, 3.25)
J1=2.79 (0.36, 21.95)
J2=Ever Had Blood Test
J3=1.02 (0.37, 2.79)
A1 A2 Abnormal PSA

A3 White Black White
A4 (n=13) (n=30) (n=l7)
A5 OR (95% CI) OR (95% CI) OR (95% CI)

A6
A7 A8 0.31 (0.04, 2.41) 1.82 (0.72, 4.61) 0.47 (0.10, 2.09)
A9 B1 0.97 (0.21, 4.54) 2.03 (0.80, 5.15) B2
B3
B4
B5 B6 B7 0.88 (0.20, 3.82) 2.55 (0.68, 9.53)
B8 B9 1.11 (0.14, 8.96) 0.91 (0.12, 7.00) 0.78 (0.10, 6.17)
C1 C2 C3 1.29 (0.58, 2.89) 1.85 (0.39, 8.65)
C4
C5 C6 1.99 (0.42, 9.48) 1.31 (0.55, 3.13) C7
C8 C9 D1 D2 D3
D4
D5 D6 0.62 (0.08, 4.90) 1.36 (0.57, 3.25) 1.55 (0.43, 5.67)
D7 D8 D9 1.32 (0.30, 5.83) E1
E2 E3 E4 1.98 (0.57, 6.91) E5
E6 E7 E8 0.86 (0.11, 6.63) 3.03 (0.34,26.71)
E9 F1 1.47 (0.47, 4.61) 0.65 (0.26, 1.62) 1.07 (0.40, 2.86)
F2
F3 F4 1.04 (0.94, 1.16) F5 F6
F7
F8 F9 G1 G2 1.03 (0.19, 5.67)
G3 G4 1.35 (0.29, 6.35) 0.92 (0.20, 4.25) 0.55 (0.18, 1.67)
G5 G6 1.39 (0.30, 6.57) G7 G8
G9
H1 H2 0.77 (0.10, 6.13) 0.79 (0.30, 2.12) 1.19 (0.26, 5.44)
H3
H4 H5 0.43 (0.10, 1.98) H6 2.04 (0.76, 5.47)
H7 H8 0.44 (0.10, 2.02) H9 1.00 (0.34, 2.94)
I1
I2 I3 1.38 (0.37, 5.20) 1.76 (0.69, 4.45) 0.59 (0.13, 2.64)
I4
I5 I6 0.53 (0.14, 1.95) 1.42 (0.68, 2.97) 1.67 (0.63, 4.47)
I7
I8 I9 J1 1.24 (0.56, 2.75) 0.71 (0.22, 2.25)
J2 J3 1.64 (0.54, 5.03) 2.10 (0.97, 4.54) 1.21 (0.45, 3.26)

[sup*] Significant at p<0.05
Table 4 Multivariate logistic regressions for associations between abnormal DRE and PSA and occupational exposures among Blacks and Whites, adjusted for significant variables from univariate analyses.
Legend for chart:

A1=Variables
A2=Exposure
A3=Pesticides
A4=Motor oil, gasoline,
A5=turpentine, or paints
A6=Occupations
A7=Tire/Rubber Industry
A8=Battery Industry
A9=Janitorial/Cleaning
B1=Grounds Maintenance
B2=Industry
B3=Manufacturing Food
B4=Products
B5=Paper/Wood Industry
B6=Entertainment/Recreation
B7=Tobacco Industry
B8=Medicine/Science
B9=Textile Industry
C1=Abnormal DRE
C2=Black
C3=OR (95% CI)
C4=2.39 (0.90, 6.34)[sup1]
C5=2.14 (0.75, 6.12)[sup1]
C6=0.56 (0.07, 4.30)[sup1]
C7=____________
C8=1.62 (0.68, 3.83)[sup1]
C9=1.34 (0.51, 3.50)[sup1]
D1=1.12 (0.25, 5.09)[sup1]
D2=1.19 (0.43, 3.32)[sup1]
D3=____________
D4=0.78 (0.10, 6.19)[sup1]
D5=____________
D6=1.08 (0.41, 2.86)[sup1]
D7=White
D8=OR (95% CI)
D9=0.26 (0.03, 2.09)[sup2]
E1=0.85 (0.18, 4.09)[sup2]
E2=____________
E3=1.28 (0.15, 10.54)[sup6]
E4=____________
E5=2.03 (0.42, 9.76)[sup5]
E6=____________
E7=0.58 (0.07, 4.66)[sup7]
E8=4.17 (0.80, 21.79)[sup5]
E9=____________
F1=____________
F2=1.53 (0.48,4.88)[sup6]
F3=____________
F4=____________
F5=____________
F6=____________
F7=____________
F8=____________
F9=____________
A1 C1 Abnormal PSA

C2 D7 Black White
C3 D8 OR (95% CI) OR (95% CI)

A2
A3 C4 D9 2.06 (0.74, 5.72)[sup3] F4
A4 C5 E1 2.18 (0.75, 6.33)[sup4] F5
A5
A6
A7 C6 E2 1.14 (0.24, 5.35)[sup3] 5.14 (1.19, 22.16)[sup5]
A8 C7 E3 1.41 (0.16, 12.05)[sup3] 0.55 (0.06, 4.72)[sup5]
A9 C8 E4 0.91 (0.37, 2.27)[sup4] 2.07 (0.41, 10.57)[sup5]
B1 C9 E5 1.10 (0.43, 2.82)[sup3] F6
B2
B3 D1 E6 F3 F7
B4
B5 D2 E7 1.17 (0.46, 3.01)[sup3] 1.25 (0.32, 4.80)[sup8]
B6 D3 E8 1.93 (0.38, 9.82)[sup9] F8
B7 D4 E9 1.99 (0.50,7.98)[sup10] F9
B8 D5 F1 0.95 (0.11, 8.64)[sup4] 2.67(0.28, 25.96)[sup8]
B9 D6 F2 1.15 (0.43, 3.09)[sup3] 0.86 (0.31,2.36)[sup8]

[sup*] Significant at p < 0.05;
[sup1] Adjusted for age and marital status;
[sup2] Adjusted for entertainment/recreation industry;
[sup3] Adjusted for age, education, and urinary symptoms;
[sup4] Adjusted for age, income, education, marital status, and
urinary symptoms;
[sup5] Adjusted for age and ever smoker;
[sup6] Adjusted for previous blood test and ever smoker;
[sup7] Adjusted for age, previous blood test, and ever smoker;
[sup8] Adjusted for age;
[sup9] Adjusted for age, education, urinary symptoms, and
family history of prostate cancer;
[sup10] Adjusted for age, income, education, and urinary symptoms.
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By Sally Weinrich, PHD, S. Weinrich, RN, PhD, FAAN, Professor, School of Nursing, University of Louisville, Kentucky, 40206.; Jennifer Waller, PHD, J. Waller, Ph.D., Biostatistician, Office of Biostatistics, Medical College of Georgia, Augusta, GA 30912-4900.; Paloma Greenwald, BSN, Paloma Greenwald, BSN, nursing student, College of Nursing, University of South Carolina, Columbia, SC 29208.; Martin Weinrich, PHD, M.C. Weinrich Ph.D., Biostatistician, Center for Health Services & Policy Research Carmichael Building #110 D, 512 South Hancock. University of Louisville, Louisville, KY, 40202. and Kristan Aronson, PHD, K. J. Aronson, PhD. Department of Community Health and Epidemiology, Queen's University, Kingston, Ontario, Canada, K7L3N6.

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