ObjectivesGestational diabetes mellitus (GDM) is associated with a higher risk for adverse health outcomes during pregnancy and delivery for both mothers and babies. This study aims to assess the short-term health and economic burden of GDM in China in 2015.DesignUsing TreeAge Pro, an analytical decision model was built to estimate the incremental costs and quality-of-life loss due to GDM, in comparison with pregnancy without GDM from the 28th gestational week until and including childbirth. The model was populated with probabilities and costs based on current literature, clinical guidelines, price lists and expert interviews. Deterministic and probabilistic sensitivity analyses were performed to test the robustness of the results.ParticipantsChinese population who gave birth in 2015.ResultsOn average, the cost of a pregnancy with GDM was ¥6677.37 (in 2015 international $1929.87) more (+95%) than a pregnancy without GDM, due to additional expenses during both the pregnancy and delivery: ¥4421.49 for GDM diagnosis and treatment, ¥1340.94 (+26%) for the mother’s complications and ¥914.94 (+52%) for neonatal complications. In China, 16.5 million babies were born in 2015. Given a GDM prevalence of 17.5%, the number of pregnancies affected by GDM was estimated at 2.90 million in 2015. Therefore, the annual societal economic burden of GDM was estimated to be ¥19.36 billion (international $5.59 billion). Sensitivity analyses were used to confirm the robustness of the results. Incremental health losses were estimated to be approximately 260 000 quality-adjusted life years.ConclusionIn China, the GDM economic burden is significant, even in the short-term perspective and deserves more attention and awareness. Our findings indicate a clear need to implement GDM prevention and treatment strategies at a national level in order to reduce the economic and health burden at both the population and individual levels.

This study examined records of 2,566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively.ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

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