gms | German Medical Science

MAINZ//2011: 56. GMDS-Jahrestagung und 6. DGEpi-Jahrestagung

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V.
Deutsche Gesellschaft für Epidemiologie e. V.

26. - 29.09.2011 in Mainz

How to estimate lung cancer mortality in low income countries? Example Sub-Saharan Africa

Meeting Abstract

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  • Volker Winkler - Institute of Public Health, Heidelberg
  • Jördis J. Ott - WHO, Geneva
  • Melanie Cowan - WHO, Geneva
  • Heiko Becher - Institute of Public Health, Heidelberg

Mainz//2011. 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz, 26.-29.09.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11gmds192

doi: 10.3205/11gmds192, urn:nbn:de:0183-11gmds1923

Published: September 20, 2011

© 2011 Winkler et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Background: Health policy planning requires reliable health information in order to assign resources and prevent mortality. In this respect, valid information on cause-specific current and future mortality is crucial. Lung cancer is the most frequent cancer death among men in the world with an estimated age-adjusted mortality rate of 23.0 per 100,000 in the year 2008. Tobacco smoking has for long been identified as the major risk factor for lung cancer causing more than 90% of all cases in developed countries. Regression models and Bayesian approaches of the age-period-cohort models are common methods to estimate current and future mortality. They are based on past mortality data as obtained from death registration systems, which are, however, not existent in many low income regions. Information on global lung cancer mortality is available from WHO's "The global burden of disease project (2004 [update 2008])" (GBD) and from IARC's "GLOBOCAN 2008". The methods used include assessment of disease-specific mortality as a fraction of all cause mortality and extrapolation of cancer mortality as obtained from local cancer registry data. We developed a method to estimate current and future lung cancer mortality based on smoking prevalence data.

Population and methods: We compare estimated numbers of lung cancer deaths for six selected low income countries of Sub-Saharan Africa (Benin, Malawi, Mozambique, Niger, Sierra Leone, and Swaziland) with numbers published by WHO and IARC databases. Our method uses representative national smoking prevalence data of those countries as obtained from WHO STEPS surveys. Applying UN population figures, we also project future lung cancer mortality for these countries.

Results: First results show immense differences in estimated numbers of lung cancer deaths. For 2008, we estimate 517 and 142 male lung cancer deaths occurring in Mozambique and Benin, respectively. Corresponding estimates given by GLOBOCAN are 155 and 69 deaths and by WHO 155 and 129 deaths.

Discussion: Our findings suggest an underestimation of lung cancer mortality in Sub-Saharan Africa, the selected African countries which might be true for other tobacco-related diseases and low income regions. Demographic changes may additionally increase the public health impact of tobacco smoking.


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