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An estimate of intensity (none 5 0, low 5 1, medium 5 2, high 5 3) and probability (none 5 0, low 5 1, medium 5 2, high 5 3) of exposure to asbestos was developed for each 3-digit occupation and industry code. Intensity of exposure was estimated based upon literature information [Parmeggiani, 1985], computerized databases (OSHA files, NIOSH inspection data base), unpublished industrial hygiene reports, and personal experience.

The probability index associated with a given occupation or industry 1980 Census code was estimated based on the proportion of exposed workers within the job title or industry under consideration, and the number of other occupations or industries coded likewise. In addition, occupations were characterized into two groups depending upon the sources of exposure. If exposure was determined by the occupation itself regardless of industry (e.g., insulators), final intensity and probability scores were obtained by squaring the respective occupational levels.

If exposure was determined by both occupation and industry (e.g., maintenance workers in shipyards), intensity and 10 Cocco and Dosemeci probability scores resulted from multiplying the respective levels of occupation and industry. The final scores of probability and intensity of exposure were further grouped in four categories (none 5 0, low 5 1–2, medium 5 4, high ( 6). Cut points were selected a priori, with the highest category defined by a score of 6 to increase the statistical power. As excluding any probability of exposure to asbestos was possible only for 1.5% of the study population (70/7227), due to the widespread use of asbestos in the past, the unexposed reference group included also subjects with low probability and low intensity exposure in order to provide more stable risk estimates.

Odds ratios (ORs) were estimated by logistic regression and 95% confidence intervals (95% C.I.) by the Wald method using the GMBO program in the Epicure software package [Preston et al., 1990]. Covariates in the logistic regression model included age (continuous), marital status (never married versus ever married), socio-economic status (five categories, based on the Green’s score for specific occupations [Green, 1970]), metropolitan vs. non-metropolitan residence, and ethnic origin (North America and Europe vs. South America and Africa).

The statistical significance of the linear trend by increasing intensity and probability of exposure to asbestos was tested by dividing the regression coefficients of the variables assumed as non-categorical by their standard error to generate a Z statistic. Under the null hypothesis, this test behaves as a normal standard deviate [Breslow and Day, 1980]. Two-tailed p values were considered throughout this article.