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Background: Leukemias are a group of life-threatening malignant disorders of the blood and bone marrow. The incidence of leukemia varies by pathological types and among different populations. Methods: We retrieved the incidence data for leukemia by sex, age, location, calendar year, and type from the Global Burden of Disease online database. The estimated average percentage change (EAPC) was used to quantify the trends of the age-standardized incidence rate (ASIR) of leukemia from 1990 to 2017. Results: Globally, while the number of newly diagnosed leukemia cases increased from 354.5 thousand in 1990 to 518.5 thousand in 2017, the ASIR decreased by 0.43% per year. The number of acute lymphoblastic leukemia (ALL) cases worldwide increased from 49.1 thousand in 1990 to 64.2 thousand in 2017, whereas the ASIR experienced a decrease (EAPC = − 0.08, 95% CI − 0.15, − 0.02). Between 1990 and 2017, there were 55, 29, and 111 countries or territories that experienced a significant increase, remained stable, and experienced a significant decrease in ASIR of ALL, respectively. The case of chronic lymphocytic leukemia (CLL) has increased more than twice between 1990 and 2017. The ASIR of CLL increased by 0.46% per year from 1990 to 2017. More than 85% of all countries saw an increase in ASIR of CLL. In 1990, acute myeloid leukemia (AML) accounted for 18.0% of the total leukemia cases worldwide. This proportion increased to 23.1% in 2017. The ASIR of AML increased from 1.35/100,000 to 1.54/100,000, with an EAPC of 0.56 (95% CI 0.49, 0.62). A total of 127 countries or territories experienced a significant increase in the ASIR of AML. The number of chronic myeloid leukemia (CML) cases increased from 31.8 thousand in 1990 to 34.2 thousand in 2017. The ASIR of CML decreased from 0.75/100,000 to 0.43/100,000. A total of 141 countries or territories saw a decrease in ASIR of CML. Conclusions: A significant decrease in leukemia incidence was observed between 1990 and 2017. However, in the same period, the incidence rates of AML and CLL significantly increased in most countries, suggesting that both types of leukemia might become a major global public health concern.

Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. Conclusions Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.

Viral hepatitis is a major public health concern in China, but data on national epidemiological characteristics are lacking. We collected reporting incidence data on hepatitis B virus (HBV) and hepatitis C virus (HCV) infections in China from 2004 to 2014. Empirical mode decomposition (EMD) was performed to accurately describe the reporting incidence trends of HBV and HCV. A mathematical model was used to estimate the relative change in incidence across provinces and age groups. Nationwide, a total of 916 426 hepatitis B cases and 39 381 hepatitis C cases were recorded in 2004; the reporting incidences of HBV and HCV were 70.50/100 000 and 3.03/100 000, respectively. The overall relative changes in HBV and HCV reporting incidences in China from 2004 to 2014 were 0.98 (95% CI 0.96-1.00, P = .082) and 1.16 (95% CI 1.12-1.20, P < .001), respectively. Thirteen provinces experienced decline in HBV reporting incidence. Most provinces exhibited an increasing trend in HCV reporting incidence. People aged ≤24 displayed a significant descending trend in HBV reporting incidence; people aged ≥55 exhibited a significant increasing trend. For HCV infection, the reporting incidence increased in all age groups except the 10-14 age group. In China, the majority of provinces have experienced decline or remained stable in HBV infection but show significant increases in HCV infection. Children and adolescents are well protected from HBV infection, while relatively higher increasing rates are found among older people. HCV is much more prevalent among older people, although its emergence has shifted to younger age groups.

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