Oumin Shi

BACKGROUND: Observational studies suggest that shift work may be associated with diabetes mellitus (DM). However, the results are inconsistent. No systematic reviews have applied quantitative techniques to compute summary risk estimates. OBJECTIVES: To conduct a meta-analysis of observational studies assessing the association between shift work and the risk of DM. METHODS: Relevant studies were identified by a search of PubMed, Embase, Web of Science and ProQuest Dissertation and Theses databases to April 2014. We also reviewed reference lists from retrieved articles. We included observational studies that reported OR with 95% CIs for the association between shift work and the risk of DM. Two authors independently extracted data and assessed the study quality. RESULTS: Twelve studies with 28 independent reports involving 226 652 participants and 14 595 patients with DM were included. A pooled adjusted OR for the association between ever exposure to shift work and DM risk was 1.09 (95% CI 1.05 to 1.12; p=0.014; I(2)=40.9%). Subgroup analyses suggested a stronger association between shift work and DM for men (OR=1.37, 95% CI 1.20 to 1.56) than for women (OR=1.09, 95% CI 1.04 to 1.14) (p for interaction=0.01). All shift work schedules with the exception of mixed shifts and evening shifts were associated with a statistically higher risk of DM than normal daytime schedules, and the difference among those shift work schedules was significant (p for interaction=0.04). CONCLUSIONS: Shift work is associated with an increased risk of DM. The increase was significantly higher among men and the rotating shift group, which warrants further studies.

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: Identifying the temporal trends of kidney cancer (KC) incidence in both the past and the future at the global and national levels is critical for KC prevention. METHODS: We retrieved annual KC case data between 1990 and 2017 from the Global Burden of Disease (GBD) online database. The average annual percentage change (AAPC) was used to quantify the temporal trends of KC age-standardized incidence rates (ASRs) from 1990 to 2017. Bayesian age-period-cohort models were used to predict KC incidence through 2030. RESULTS: Worldwide, the number of newly diagnosed KC cases increased from 207.3 thousand in 1990 to 393.0 thousand in 2017. The KC ASR increased from 4.72 per 100,000 to 4.94 per 100,000 during the same period. Between 2018 and 2030, the number of KC cases is projected to increase further to 475.4 thousand (95% highest density interval [HDI] 423.9, 526.9). The KC ASR is predicted to decrease slightly to 4.46 per 100,000 (95% HDI 4.06, 4.86). A total of 90, 2, and 80 countries or territories are projected to experience increases, remain stable, and experience decreases in KC ASR between 2018 and 2030, respectively. In most developed countries, the KC incidence is forecasted to decrease irrespective of past trends. In most developing countries, the KC incidence is predicted to increase persistently through 2030. CONCLUSIONS: KC incidence is predicted to decrease in the next decade, and this predicted decrease is mainly driven by the decreases in developed countries. More attention should be placed on developing countries.

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.