Fang et al. specified) and the risk of ALS was assessed sepa- rately. Relative risk variations by service branch, duration of service (1–2, 3–5, and > 5 years), and wartime service (1917–1918 World War I, 1942–1944 World War II, 1950–1953 Korean War, or 1965–1973 Vietnam War) were fur- ther investigated. Participants reported separately exposure to any of 21 specific agents in response to the question, “On any of your jobs, were you exposed 10 times or more to . . . .” Those who responded positively were asked about total years exposed and average days of exposure per year. The latter two variables were multi- plied to calculate lifetime days of exposure, and categorized as “1–399,” “400–1,999,” and “≥ 2,000” days, giving approximately equal numbers in each category for most exposures. Specific chemical exposures likely to result from these agents were determined by an industrial hygienist a priori [see Supplemental Material, Table 1, available online (doi:10.1289/ ehp.0900580.S1 via http://dx.doi.org/)]. We identified occupations possibly involv- ing formaldehyde exposure (Agency for Toxic Substances and Disease Registry 1999), and an industrial hygienist created an a priori three- level scale for probability of exposure in these occupations [0–1, 1, and 2; see Supplemental Material, Table 2 (doi:10.1289/ehp.0900580. S1)]. We calculated weighted hours of formal- dehyde exposure from each job as the product of job duration (assuming 50 working weeks per year) and weekly working hours weighted by the three-level scale (weights of 0.5, 1, and 2 were given to levels 0–1, 1, and 2, respec- tively); these were then summed over all jobs to derive weighted lifetime hours of exposure. Finally, the lifetime hours of exposure were categorized into tertiles based on the distribu- tion among the controls. Interviewers estimated the overall quality of each interview at its conclusion. Of the Table 1. Basic characteristics of cases (n = 109) and controls (n = 253). Characteristic Age (years) 30–55 56–65 66–80 Sex Male Female Region New England Education > High school ≤ High school Smoking Never Ever Boston metropolitan area Eastern Massachusetts Total jobs [median (range)] 1388 Cases [No. (%)] 38 (34.9) 37 (33.9) 34 (31.2) 66 (60.6) 43 (39.4) 32 (29.4) 28 (25.7) 49 (44.9) 71 (65.1) 38 (34.9) 32 (29.4) 77 (70.6) 4 (1–10) Controls [No. (%)] 83 (32.8) 73 (28.9) 97 (38.3) 156 (61.7) 97 (38.3) 101 (39.9) 53 (21.0) 99 (39.1) 200 (79.0) 53 (21.0) 105 (41.5) 148 (58.5) 4 (1–13) 362 interviews, 264 were classified as high quality, 93 as generally reliable, and five as questionable. Statistical analysis. We used odds ratios (ORs) and their corresponding 95% confi- dence intervals (CIs) derived from uncondi- tional logistic regression models to assess the relative risks of ALS. Individuals with more than one occupation were included in multiple categories. The matching variables age, sex, and area of residence were included in all models. Smoking history (ever or never) and educa- tional level (≤ high school or > high school) were included as potential confounders. We tested potential linear trends of ORs with the lifetime days of exposure to each workplace agent as well as exposure probabil- ity and weighted exposure duration of formal- dehyde using a continuous variable with four values (0, 1, 2, and 3) representing unexposed and the three exposure categories. To assess potential interactions between smoking and the workplace exposures, we conducted analy- ses stratified by smoking (ever/never). We also evaluated the significance of the interac- tions by adding a term for the interaction of a specific exposure and smoking to the models. We report two-tailed p-values and consider any p-value < 0.05 as statistically significant. We used SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) for all statistical analyses. Results Characteristics of cases and controls are pre- sented in Table 1. The median age at diagno- sis for cases was 60 years (range, 30–79 years) and the median age at 2 years before interview for controls was 59 years (range, 29–78 years). Diagnosis was made 14.3 months after the onset of symptoms on average. Overall, 27 (25%) of the cases had a bulbar onset and Table 2. Occupations and the risk of ALS.a 82 (75%) had a trunk or limb onset. Cases had a median of four different jobs (range, 1–10) before ALS diagnosis. Controls also had a median of four jobs (range, 1–13) up to 2 years before the date of interview (Table 1). Construction trades and precision pro- duction were both associated with a higher risk of ALS (OR = 2.5; 95% CI, 1.0–5.8 and OR = 2.2; 95% CI, 1.1–4.4, respectively) (Table 2). Construction workers exclud- ing supervisors had a 2.9-fold risk of ALS (95% CI, 1.2–7.2), whereas precision metal- working in particular was associated with a 3.5-fold risk of ALS (95% CI, 1.2–10.5). Transportation and material-moving workers had a nonsignificantly higher risk of ALS (OR = 1.9; 95% CI, 0.9–4.3); those using motor vehicles had an even higher risk (OR = 2.2; 95% CI, 0.9–5.6). Service-related occupa- tions, excluding private household and pro- tective service, were associated with a lower risk of ALS (OR = 0.3; 95% CI, 0.1–0.7). The occupational category “farming, forestry, and fishing” was not associated with the risk of ALS (OR = 1.0; 95% CI, 0.2–4.2). In total, 24 cases (19%) and 49 controls (22%) had served in the military. Military ser- vice overall was not associated with the risk of ALS (OR = 1.2; 95% CI, 0.7–2.4). No clear variation of the ORs was noted when military service was subgrouped by branch, duration, or wartime service (data not shown). Table 3 shows the association between workplace exposures and ALS. We include for comparison previously reported data show- ing an association of ALS with lead exposure (Kamel et al. 2002). Exposure to paint strip- pers; cutting, cooling, or lubricating oils; anti- freeze or coolants; mineral or white spirits; or dry cleaning agents was each associated with a 1.6- to 1.9-fold risk of ALS, although only the No. of controls No. of cases Occupation (n = 253) (n = 109) OR (95% CI)b Executive, administrative, and managerial 71 25 0.9 (0.5–1.5) Management related 36 6 0.4 (0.2–1.1) Engineers, architects, and surveyors 92 37 1.1 (0.7–1.9) Technician and related support 39 8 0.5 (0.2–1.1) Sales 68 27 1.0 (0.6–1.7) Administrative support, including clerical 80 42 1.4 (0.9–2.4) Private household 8 2 0.5 (0.1–2.7) Protective service 10 6 1.3 (0.4–3.8) Service, excluding household or protective 43 8 0.3 (0.1–0.7) Farming, forestry, and fishing 6 3 1.0 (0.2–4.2) Mechanics and repairers 19 14 1.6 (0.7–3.4) Construction trades 13 14 2.5 (1.0–5.8) Precision production 21 19 2.2 (1.1–4.4) Plant and system operators 1 0 — Machine operators, assemblers, and inspectors 38 18 0.8 (0.4–1.6) Transportation and material moving 16 14 1.9 (0.9–4.3) Handlers, equipment cleaners, helpers, and laborers 21 8 0.7 (0.3–1.6) aFor analysis of each occupation, individuals who never had that occupation served as the reference group. Occupations were categorized using the 1990 Census Industrial and Occupational Classification Codes (U.S. Bureau of Labor Statistics 2008). bCalculated from logistic regression models. All models were adjusted for age (< 56, 56–65, and > 65 years), sex, area of residence (Boston metropolitan area, eastern Massachusetts, and rest of New England), smoking, and educational level. ovlume 117 | number 9 | September 2009 • Environmental Health Perspectives