Kovalevskiy E.V.,Russian Academy of Medical Sciences |
Schonfeld S.J.,International Agency for Research on Cancer IARC |
Feletto E.,International Agency for Research on Cancer IARC |
Moissonnier M.,International Agency for Research on Cancer IARC |
And 3 more authors.
Environmental Health: A Global Access Science Source | Year: 2016
Background: The Sverdlovsk region of the Russian Federation is characterised by its abundance of natural resources and industries. Located in this region, Asbest city is situated next to one of the largest open-pit chrysotile asbestos mines currently operational; many city residents are employed in activities related to mining and processing of chrysotile. We compared mortality rates from 1997 to 2010 in Asbest city to the remaining Sverdlovsk region, with additional analyses conducted for site-specific cancer mortality. Methods: Population and mortality data for Asbest city and Sverdlovsk region were used to estimate crude and age-specific rates by gender for the entire period and for each calendar year. Age-standardized mortality rates were also calculated for the adult population (20+) and Poisson regression was used to estimate standardized mortality ratios, overall and by gender. Results: During the period of 1997 to 2010, there were similar mortality rates overall in Asbest and the Sverdlovsk region. However, there were higher rates of cancer mortality (18 % males; 21 % females) and digestive diseases (21 % males; 40 % females) in Asbest and lower rates of unknown/ill-defined in Asbest (60 % males; 47 % females). Circulatory disease mortality was slightly lower in Asbest. Cancer mortality was higher for men in Asbest from oesophageal, urinary tract and lung cancers compared to the Sverdlovsk region. In women, cancer mortality was higher for women in Asbest from stomach, colon, lung and breast cancers compared to the Sverdlovsk region. Conclusions: This large population-based analysis indicates interesting differences but studies with individual exposure information are needed to understand the underlying factors. © 2016 Kovalevskiy et al. Source