Estimation of the global burden of mesothelioma deaths from incomplete national mortality data. Issue 12 (2nd September 2017)

• Record Type: Journal Article
• Authors: Odgerel, Chimed-Ochir; Takahashi, Ken; Sorahan, Tom; Driscoll, Tim; Fitzmaurice, Christina; Yoko-o, Makoto; Sawanyawisuth, Kittisak; Furuya, Sugio; Tanaka, Fumihiro; Horie, Seichi; Zandwijk, Nico van; Takala, Jukka
• Is Part Of: Occupational and environmental medicine. Volume 74:Issue 12(2017)
• Page Start: 851
• Page End: 858
• Publication Date: 2017-09-02
• Subjects: asbestos -- mesothelioma -- statistics; Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2017-104298
• Languages: English
• ISSNs: 1351-0711

Abstract

Abstract : Background: Mesothelioma is increasingly recognised as a global health issue and the assessment of its global burden is warranted. Objectives: To descriptively analyse national mortality data and to use reported and estimated data to calculate the global burden of mesothelioma deaths. Methods: For the study period of 1994 to 2014, we grouped 230 countries into 59 countries with quality mesothelioma mortality data suitable to be used for reference rates, 45 countries with poor quality data and 126 countries with no data, based on the availability of data in the WHO Mortality Database. To estimate global deaths, we extrapolated the gender-specific and age-specific mortality rates of the countries with quality data to all other countries. Results: The global numbers and rates of mesothelioma deaths have increased over time. The 59 countries with quality data recorded 15 011 mesothelioma deaths per year over the 3 most recent years with available data (equivalent to 9.9 deaths per million per year). From these reference data, we extrapolated the global mesothelioma deaths to be 38 400 per year, based on extrapolations for asbestos use. Conclusions: Although the validity of our extrapolation method depends on the adequate identification of quality mesothelioma data and appropriate adjustment for other variables, our estimates can be updated, refined and verified because they are based on commonly accessible data and are derived using a straightforward algorithm. Our estimates are within the range of previously reported values but higher than the most recently reported values.