BMC Public Health 2008, 8:21 http://www.biomedcentral.com/1471-2458/8/21 An internal comparison, using the above mentioned refer- ence group, was carried out through a Poisson regression model for main causes of death. where DVMAX and DFMAX were obtained by fitting a maxi- mal model, i.e., a model with a high level of interactions, as suggested by Aitkin et al. [12]. Potential confounding factors considered in the analysis were age (five-year classes, 25–75+), calendar period of follow-up (1972–1974, 1975–1999 by five-year periods), age at hiring (2 levels: before age 25, 25+), year of hiring (before 1973, 1973 or later), duration of exposure (i.e., duration of employment; 3 levels: 0–4; 5–9; 10+), and latency (i.e., time since first hiring; 2 levels: 0–9; 10+). Data were grouped into cells according to the level of the explanatory variables described above. The model used was: ln(Ok) = ln(Ek) + α + β iXi + εk where Ok are the observed deaths for a specific cause in the kth cell, and Ek are the expected deaths in the kth cell accord- ing to age, calendar year and cause specific mortality rates of the Italian male population (ln() are their natural log- arithms); exp(α) is the Standardized Mortality Ratio (SMR) compared to the Italian population for persons falling in the baseline category (i.e., with all the variables set at the reference level); Xi is a matrix of indicator varia- bles that specify the levels of the explanatory variables defining each cell; βi is a vector of unknown parameters to be estimated from the data, such that exp(βi) represents the Relative Risk (RR) related to each explanatory variable with respect to its own reference level; εk is the random error. Each job category RR estimated by this model repre- sents the ratio between the SMR of the exposed group and the SMR of the unexposed one for each specific cause of death. As suggested by Breslow and Day, the linkage of the method of indirect standardization to the multivariate regression analysis improves the efficiency of the statisti- cal modeling [11]. The impact of the inclusion of each possible confounder in the regression models was tested through the likeli- hood-ratio test and by evaluating the change in the esti- mated RRs and their standard errors. In order to avoid biased estimation of the standard errors of the parameters and to improve the goodness-of-fit of the selected model, when the ratio between the residual deviance (DV) and its degrees of freedom (DF) was far from 1 (i.e., in presence of over- or underdispersion), we refitted the model by introducing the following scale parameter: DVMAX/DFMAX Due to the low number of cases of liver and brain tumors and for liver cirrhosis, Bayesian estimates of the mean of the posterior distribution of RR (RRpm) for these diseases were obtained by means of the Monte Carlo – Markov Chain method [13]. Confidence intervals at the arbitrary levels of both 95% (95% CI) and 95% Bayesian credibility interval (95% BCI) between the 2.5th and 97.5th percentile of the poste- rior distribution of RR, were estimated as appropriate. To assess the goodness-of-fit of the statistical models, stand- ard diagnostic procedures were performed. Poisson mod- els were fitted to the data using STATA statistical software [14]. The attributable number of deaths (AD) among exposed workers was estimated according to the following equa- tion: ⎛ RRe −1 ⎞ AD = d ⋅ ⎜ ⎟ ⎝ RRe ⎠ where RRe is the RR of the exposed (sub)group and d is the number of observed deaths. Results The numbers of workers, mean age at hiring and person- years by age for each subgroup of workers (autoclave work- ers, PVC baggers, PVC compound workers, other blue collar workers, and technicians and clerks) are reported in table 2. Numbers of observed deaths (Obs), RRs and 95% CIs (or RRpms and 95% BCIs, as appropriate [see Methods]) by job category using technicians and clerks as a reference are shown in table 3. The total blue collar workforce (last column of tab. 3) experienced significantly increased mortality from all causes (RR = 1.55, 95% CI 1.03–2.35; 229 deaths); RRs for all tumors and for each specific cause of death considered were increased, but not significantly. The comparison of each subgroup of blue collar workers with the internal reference revealed different risk patterns. In fact, RR estimates were significantly increased for liver cancer (RRpm = 9.57, 95% CI: 3.71–24.68) among auto- clave workers, for all causes (RR = 1.72, 95% CI: 1.04–2.83) and cardiovascular diseases (RR = 2.25, 95% CI: 1.08–4.70) among PVC baggers, and for all causes (RR = 1.71, 95%CI: 1.09–2.67) among PVC compound workers. Page 3 of 8 (page number not for citation purposes)