and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEWThe GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs).FINDINGS In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCEThe results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

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Given the dynamic relationship between oral and general health, dental care must not be neglected even during a public health emergency. Nevertheless, the fear of contracting the infection appears to have caused instances of dental treatment avoidance. In these times of uncertainty, regulatory and public health organizations have made numerous and sometimes controversial recommendations to practitioners and to the public about how to secure their oral health care needs. Dentists, as advocates of oral health, should actively maintain their practices while considering local epidemiological reports and recommendations regarding prevention of SARS-CoV-2 infection. Providing appropriate safety measures, accurate triage and prioritization of patients, notice to susceptible communities, remote health care delivery when appropriate, and epidemiological reports of COVID-19 (whenever possible) are all critical considerations for dental practitioners.

Aim This systematic review sought to assess and scrutinise the validity and practicality of published and preprint reports of prediction models for the diagnosis of coronavirus disease 2019 (COVID-19) in patients with suspected infection, for prognosis of patients with COVID-19, and for identifying individuals in the general population at increased risk of infection with COVID-19 or being hospitalised with the illness. Data sources A systematic, online search was conducted in PubMed and Embase. In order to do so, the authors used Ovid as the host platform for these two databases and also investigated bioRxiv, medRxiv and arXiv as repositories for the preprints of studies. A public living systematic review list of COVID-19-related studies was used as the baseline searching platform (Institute of Social and Preventive Medicine's repository for living evidence on COVID-19). Study selection Studies which developed or validated a multivariable prediction model related to COVID-19 patients' data (individual level data) were included. The authors did not put any restrictions on the models included in their study regarding the model setting, prediction horizon or outcomes. Data extraction and synthesis Checklists of critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST) were used to guide developing of a standardised data extraction form. Each model's predictive performance was extracted by using any summaries of discrimination and calibration. All these steps were done according to the aspects of the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and preferred reporting items for systematic reviews and meta-analyses (PRISMA). Results One hundred and forty-five prediction models (107 studies) were selected for data extraction and critical appraisal. The most common predictors of diagnosis and prognosis of COVID-19 were age, body temperature, lymphocyte count and lung imaging characteristics. Influenza-like symptoms and neutrophil count were regularly predictive in diagnostic models, while comorbidities, sex, C-reactive protein and creatinine were common prognostic items. C-indices (a measure of discrimination for models) ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in the prognostic models. All the included studies were reported to have high risks of bias. Conclusions Overall, this study did not recommend applying any of the predictive models in clinical practice yet. High risk of bias, reporting problems and (probably) optimistic reported performances are all among the reasons for the previous conclusion. Prompt actions regarding accurate data sharing and international collaborations are required to achieve more rigorous ...