Weimin Li

Importance: Cancers are a leading cause of mortality, accounting for nearly 10 million annual deaths worldwide, or 1 in 6 deaths. Cancers also negatively affect countries' economic growth. However, the global economic cost of cancers and its worldwide distribution have yet to be studied. Objective: To estimate and project the economic cost of 29 cancers in 204 countries and territories. Design, Setting, and Participants: A decision analytical model that incorporates economic feedback in assessing health outcomes associated with the labor force and investment. A macroeconomic model was used to account for (1) the association of cancer-related mortality and morbidity with labor supply; (2) age-sex-specific differences in education, experience, and labor market participation of those who are affected by cancers; and (3) the diversion of cancer treatment expenses from savings and investments. Data were collected on April 25, 2022. Main Outcomes and Measures: Economic cost of 29 cancers across countries and territories. Costs are presented in international dollars at constant 2017 prices. Results: The estimated global economic cost of cancers from 2020 to 2050 is $25.2 trillion in international dollars (at constant 2017 prices), equivalent to an annual tax of 0.55% on global gross domestic product. The 5 cancers with the highest economic costs are tracheal, bronchus, and lung cancer (15.4%); colon and rectum cancer (10.9%); breast cancer (7.7%); liver cancer (6.5%); and leukemia (6.3%). China and the US face the largest economic costs of cancers in absolute terms, accounting for 24.1% and 20.8% of the total global burden, respectively. Although 75.1% of cancer deaths occur in low- and middle-income countries, their share of the economic cost of cancers is lower at 49.5%. The relative contribution of treatment costs to the total economic cost of cancers is greater in high-income countries than in low-income countries. Conclusions and Relevance: In this decision analytical modeling study, the macroeconomic cost of cancers was found to be substantial and distributed heterogeneously across cancer types, countries, and world regions. The findings suggest that global efforts to curb the ongoing burden of cancers are warranted.

BACKGROUND: The cholesteryl ester transfer protein inhibitor evacetrapib substantially raises the high-density lipoprotein (HDL) cholesterol level, reduces the low-density lipoprotein (LDL) cholesterol level, and enhances cellular cholesterol efflux capacity. We sought to determine the effect of evacetrapib on major adverse cardiovascular outcomes in patients with high-risk vascular disease. METHODS: In a multicenter, randomized, double-blind, placebo-controlled phase 3 trial, we enrolled 12,092 patients who had at least one of the following conditions: an acute coronary syndrome within the previous 30 to 365 days, cerebrovascular atherosclerotic disease, peripheral vascular arterial disease, or diabetes mellitus with coronary artery disease. Patients were randomly assigned to receive either evacetrapib at a dose of 130 mg or matching placebo, administered daily, in addition to standard medical therapy. The primary efficacy end point was the first occurrence of any component of the composite of death from cardiovascular causes, myocardial infarction, stroke, coronary revascularization, or hospitalization for unstable angina. RESULTS: At 3 months, a 31.1% decrease in the mean LDL cholesterol level was observed with evacetrapib versus a 6.0% increase with placebo, and a 133.2% increase in the mean HDL cholesterol level was seen with evacetrapib versus a 1.6% increase with placebo. After 1363 of the planned 1670 primary end-point events had occurred, the data and safety monitoring board recommended that the trial be terminated early because of a lack of efficacy. After a median of 26 months of evacetrapib or placebo, a primary end-point event occurred in 12.9% of the patients in the evacetrapib group and in 12.8% of those in the placebo group (hazard ratio, 1.01; 95% confidence interval, 0.91 to 1.11; P=0.91). CONCLUSIONS: Although the cholesteryl ester transfer protein inhibitor evacetrapib had favorable effects on established lipid biomarkers, treatment with evacetrapib did not result in a lower rate of cardiovascular events than placebo among patients with high-risk vascular disease. (Funded by Eli Lilly; ACCELERATE ClinicalTrials.gov number, NCT01687998 .).

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.