The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Quantifying the combined effects of climatic, crop and soil factors on surface resistance in a maize field

Ortega-Farias, S (Ortega-Farias, Samuel). Univ Talca, Fac Ciencias Agr, Res & Extens Ctr Irrigat & Agroclimatol CITRA, Talca, ChileLand surface evapotranspiration (ET) is the central process in hydrological cycle. The accuracy in simulating ET is affected by the calculation of underlying surface resistance. However, the surface resistance is difficult to be measured and greatly affected by climatic, crop and soil factors. How to quantify the combined effects of these factors on surface resistance is still a challenge for hydrologists. Our study attempted to construct and validate a semi-empirical surface resistance model based on the analysis of the response pattern of surface resistance to climatic resistance, leaf area index (LAI) and soil moisture. The surface resistance was derived by the re-arranged Penman-Monteith (PM) equation and the measured maize ET using eddy covariance in 2007. Results indicate that the ratio of surface resistance to climatic resistance showed a logarithmic relationship with LA!, and an exponential function as soil moisture when LAI was below 2. But the ratio was nearly constant and not sensitive to variation in LAI and soil moisture when LA! exceeded 2. Based on the relationships, a surface resistance model was further constructed and compared to the widely used Katerji-Perrier and Jarvis models over the sparse maize and grape canopy. Our resistance model combined with the PM equation improved the accuracy in estimating daily maize ET by 11% in 2007 and 4% in 2008, and vineyard ET by 7% against the Katerji-Perrier model combined with PM method, while by 32% in 2007 and 104% in 2008, and vineyard ET by 5% against the Jarvis model combined with PM method. Thus our model significantly improved the performance in simulating sparse vegetation ET and can b