Suchergebnisse

Abstract

Objectives
In Italy, the highest pleural cancer mortality and incidence have been observed among Italian regions where the 2 largest Italian shipyards were (and are) located. The objective of this study was to assess the exposure–response relationship for mesothelioma among male workers employed in the Monfalcone, Italy, shipyard.


Methods
We conducted a necropsy-based case–control study. Cases (N = 102) were mesothelioma decedents and controls were those with lung cancer (N = 84). Complete job histories were available; the lung fibre content was measured using a scanning electron microscope with X-ray fluorescence, after sample preparation according to the European Respiratory Society guidelines. Odds ratios and 95% confidence intervals of mesothelioma by fibre type and lung fibre burden, as a categorical or continuous variable, were assessed by unconditional logistic regression, adjusted for age and time since exposure cessation. Analyses for the amphibole and chrysotile lung fibre burden were mutually adjusted. We calculated a cumulative exposure index by applying a job-exposure matrix to the job histories of study cases and assessed its correlation with the lung fibre burden.


Results
We found an odds ratio of 22.0 (confidence intervals 5.66–85.7) for the highest lung fibre burden category (mean 43.8 million total asbestos fibres per gram of dry tissue) compared with the reference (mean 0.48). Using log10-transformed lung fibre burden, we found that the odds ratio was 3.71 (confidence intervals 2.03–6.79) for a 10-fold lung fibre burden increase. Results for the amphibole lung fibre burden were similar. Odds ratios increased over chrysotile lung fibre burden categories (P-trend = 0.025), and the odds ratio for a 10-fold increase was 4.73 (confidence intervals 0.32–70.4).


Conclusions
The cumulative exposure index was correlated with total and amphibole lung fibre burden, but not with chrysotile lung fibre burden. Mesothelioma risk was proportional to total, amphibole, and chrysotile lung fibre burden in shipyard workers.

Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but they have been used mostly for English-language data sets. Considering such a framework, the performance of an ML classification task of a Spanish-language ED visits database was tested. ED visits collected in the EDs of nine hospitals in Nicaragua were analyzed. Spanish-language, free-text discharge diagnoses were considered in the analysis. Five-hundred random forests were trained on a set of bootstrap samples of the whole data set (1,789 ED visits) to perform the classification task. For each one, after having identified optimal parameter value, the final validated model was trained on the whole bootstrapped data set and tested. The classification accuracies had a median of 0.783 (95% CI [0.779, 0.796]). Machine learning techniques seemed to be a promising opportunity for the exploitation of unstructured information reported in ED records in low- and middle-income Spanish-speaking countries.

Abstract

Objectives
This study was performed with the aim of investigating the temporal patterns and determinants associated with mortality from asbestosis among 21 cohorts of Asbestos-Cement (AC) workers who were heavily exposed to asbestos fibres.


Methods
Mortality for asbestosis was analysed for a cohort of 13 076 Italian AC workers (18.1% women). Individual cumulative asbestos exposure index was calculated by factory and period of work weighting by the different composition of asbestos used (crocidolite, amosite, and chrysotile). Two different approaches to analysis, based on Standardized Mortality Ratios (SMRs) and Age-Period-Cohort (APC) models were applied.


Results
Among the considered AC facilities, asbestos exposure was extremely high until the end of the 1970s and, due to the long latency, a peak of asbestosis mortality was observed after the 1990s. Mortality for asbestosis reached extremely high SMR values [SMR: males 508, 95% confidence interval (CI): 446–563; females 1027, 95% CI: 771–1336]. SMR increased steeply with the increasing values of cumulative asbestos exposure and with Time Since the First Exposure. APC analysis reported a clear age effect with a mortality peak at 75–80 years; the mortality for asbestosis increased in the last three quintiles of the cumulative exposure; calendar period did not have a significant temporal component while the cohort effect disappeared if we included in the model the cumulative exposure to asbestos.


Conclusions
Among heaviest exposed workers, mortality risk for asbestosis began to increase before 50 years of age. Mortality for asbestosis was mainly determined by cumulative exposure to asbestos.