Cancer incidence in a cohort of Swedish merchant seafarers between 1985 and 2011

Mesothelioma among seamen: a systematic review and meta-analysis

OBJECTIVES

Navy personnel and seafarers live and work 24 h per day in the shipboard environment and they are exposed to asbestos fibers released into the confined spaces aboard ships. We conducted a systematic review and meta-analysis to quantify the mesothelioma risk of seamen working aboard ships, either commercial or naval vessels, as compared to that of the general population.

METHODS

We carried out a literature search in MEDLINE through PubMed and EMBASE, from inception to 31 December 2021, of all studies on seamen working aboard ships, either commercial or naval vessels, characterized by exposure to asbestos and providing mesothelioma risk estimates. The Newcastle-Ottawa Scale was used to assess the quality of the studies included. The pooled standardized mortality ratio (SMR) was computed across eligible studies. The study protocol was registered on PROSPERO and reporting followed the preferred reporting items for systematic reviews and meta-analyses guidelines.

RESULTS

A total of 10 studies published from 1990 to 2020 were considered eligible and included in the systematic review and meta-analysis. All the included studies were of good quality, with a median score of seven out of nine. Overall, there were 235 mesothelioma cases/deaths in the included studies versus 115.6 expected, with a pooled SMR of 2.11 (95% confidence intervals, 1.70-2.62), in the absence of a significant between-study heterogeneity (I2 = 39%, P = 0.11).

CONCLUSION

A more than double excess risk for mesothelioma among seamen working aboard ships emerged from our meta-analysis.

Mesothelioma Risk Among Maritime Workers According to Job Title: Data From the Italian Mesothelioma Register (ReNaM)

The study describes the 466 cases of malignant mesotheliomas (MM) collected by the National Mesothelioma Register (ReNaM) in Italy in the period 1993-2018 relating to subjects with exclusive asbestos exposure in merchant or military navy. The cases among maritime workers represent 1.8% of the total cases with defined exposure registred in the ReNaM, of which 212 cases (45.4%) among merchant maritime workers and 254 cases (54.5%) among navy. The distribution by site of mesothelioma showed 453 (97.2%) MM cases of the pleura, 11 (2.3%) of the peritoneum and 2 (0.4%) of the tunica vaginalis of the testis. With regard to occupational exposure, it was classified as certain in 318 (68.2%) cases, probable in 69 (14.8%) cases and possible in 79 (16.9%) cases. Among the 23 classified jobs, the highest percentages of certain exposures are among naval engineers, motor mechanics, machine captains and sailors. Machine crew accounted for 49.3% of the cases, deck crew for 27.6%. All cases began exposure on board between 1926 and 1988. Seamen were exposed to asbestos while at sea by virtue of living onboard ships and from continual release of asbestos fibers due to the motion of a vessel. Epidemiological surveillance through the ReNaM has allowed us to verify among cases in the maritime, navy and merchant marine sectors, that in the past, subjects were exposed regardless of the ship's department where have provided service therefore all these cases must be considered as occupational diseases.

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models

We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L∞ norm and less than 6.3% in the L1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.