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Lung cancer incidence is increasing in many low-to-middle-income countries and is significantly under-reported in Africa, which could potentially mislead policy makers when prioritising disease burden. We employed an ecological correlation study design using countrylevel lung cancer incidence data and associated determinant data. Lagged prevalence of smoking and other exposure data were used to account for exposure-disease latency. A multivariable Poisson model was employed to estimate missed lung cancer in countries lacking incidence data. Projections were further refined to remove potential deaths from infectious/external competing causes. Global lung cancer incidence was much lower among females vs males (13.6 vs 34.2 per 100,000). Distinct spatial heterogeneity was observed for incident lung cancer and appeared concentrated in contiguous regions. Our model predicted a revised global lung cancer incidence in 2012 of 23.6 compared to the Globocan 2012 estimate of 23.1, amounting to ~38,101 missed cases (95% confidence interval: 28,489-47,713). The largest relative under-estimation was predicted for Africa, Central America and the Indian Ocean regions (Comoros, Madagascar, Mauritius, Mayotte, Reunion, Seychelles). Our results suggest substantial underreporting of lung cancer incidence, specifically in developing countries (e.g. Africa). The missed cost of treating these cases could amount to >US$ 130 million based on recent developing setting costs for treating earlier stage lung cancer. The full cost is not only under-estimated, but also requires substantial additional social/family inputs as evidenced in more developed settings like the European Union. This represents a major public health problem in developing settings (e.g. Africa) with limited healthcare resources.

Original publication





Geospatial health

Publication Date





School of Nursing and Public Health, University of KwaZulu-Natal, Durban.


Humans, Lung Neoplasms, Incidence, Prevalence, Models, Statistical, Bayes Theorem, Smoking, Air Pollution, Age Distribution, Sex Distribution, Adolescent, Adult, Middle Aged, Female, Male, Young Adult, Gross Domestic Product, Global Health