China has the world’s largest diabetes epidemic, which continues to increase.¹ The prevalence of diabetes in China was reported to be 0.67% in 1980 and 11.6% in the latest published nationwide estimate in 2010.^2-5 Moreover, according to the 2010 survey, the prevalence of prediabetes was 50.1%, implying that approximately 500 million Chinese adults may have had prediabetes.⁵ However, this high estimate for prediabetes raised concern over the possibility of an overestimation.^6,7 As a disorder of glucose metabolism, diabetes mellitus affects multiple organ systems and is associated with a variety of vascular and several nonvascular complications.^8-10 Ongoing reliable estimations are needed to plan effective national prevention and treatment programs for diabetes management.

Evidence of the short-term effects of ambient air pollution on the risk of ischemic stroke in low- and middle-income countries is limited and inconsistent. We aimed to examine the associations between air pollution and daily hospital admissions for ischemic stroke in China. We identified hospital admissions for ischemic stroke in 2014–2016 from the national database covering up to 0.28 billion people who received Urban Employee Basic Medical Insurance (UEBMI) in China. We examined the associations between air pollution and daily ischemic stroke admission using a two-stage method. Poisson time-series regression models were firstly fitted to estimate the effects of air pollution in each city. Random-effects meta-analyses were then conducted to combine the estimates. Meta-regression models were applied to explore potential effect modifiers. More than 2 million hospital admissions for ischemic stroke were identified in 172 cities in China. In single-pollutant models, increases of 10 μg/m³ in particulate matter with aerodynamic diameter <2.5 μm (PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) and 1 mg/m³ in carbon monoxide (CO) concentrations were associated with 0.34% (95% confidence interval [CI], 0.20%–0.48%), 1.37% (1.05%–1.70%), 1.82% (1.45%–2.19%), 0.01% (−0.14%–0.16%), and 3.24% (2.05%–4.43%) increases in hospital admissions for ischemic stroke on the same day, respectively. SO₂ and NO₂ associations remained significant in two-pollutant models, but not PM_2.5 and CO associations. The effect estimates were greater in cities with lower air pollutant levels and higher air temperatures, as well as in elderly subgroups. The main limitation of the present study was the unavailability of data on individual exposure to ambient air pollution. As the first national study in China to systematically examine the associations between short-term exposure to ambient air pollution and ischemic stroke, our findings indicate that transient increase in air pollution levels may increase the risk of ischemic stroke, which may have significant public health implications for the reduction of ischemic stroke burden in China.

Background: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs. Methods: The source database used for model development and validation is the medical information mart for intensive care (MIMIC) III. We identified adult sepsis patients using the new sepsis definition Sepsis-3. A total of 86 predictor variables consisting of demographics, laboratory tests and comorbidities were used. We employed the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM) and the traditional logistic regression (LR) method to develop prediction models. In addition, the prediction performance of the four developed models was evaluated and compared with that of an existent scoring tool – simplified acute physiology score (SAPS) II – using five different performance measures: the area under the receiver operating characteristic curve (AUROC), Brier score, sensitivity, specificity and calibration plot. Results: The records of 16,688 sepsis patients in MIMIC III were used for model training and test. Amongst them, 2949 (17.7%) patients had in-hospital death. The average AUROCs of the LASSO, RF, GBM, LR and SAPS II models were 0.829, 0.829, 0.845, 0.833 and 0.77, respectively. The Brier scores of the LASSO, RF, GBM, LR and SAPS II models were 0.108, 0.109, 0.104, 0.107 and 0.146, respectively. The calibration plots showed that the GBM, LASSO and LR models had good calibration; the RF model underestimated high-risk patients; and SAPS II had the poorest calibration. Conclusion: The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.

Objective: To estimate the risks of daily hospital admissions for cause specific major cardiovascular diseases associated with short term exposure to ambient fine particulate matter (aerodynamic diameter ≤2.5 μm; PM_2.5) pollution in China.Design: National time series study.Setting: 184 major cities in China.Population: 8 834 533 hospital admissions for cardiovascular causes in 184 Chinese cities recorded by the national database of Urban Employee Basic Medical Insurance from 1 January 2014 to 31 December 2017.Main outcome measures: Daily counts of city specific hospital admissions for primary diagnoses of ischaemic heart disease, heart failure, heart rhythm disturbances, ischaemic stroke, and haemorrhagic stroke among different demographic groups were used to estimate the associations between PM_2.5 and morbidity. An overdispersed generalised additive model was used to estimate city specific associations between PM_2.5 and cardiovascular admissions, and random effects meta-analysis used to combine the city specific estimates.Results: Over the study period, a mean of 47 hospital admissions per day (standard deviation 74) occurred for cardiovascular disease, 26 (53) for ischaemic heart disease, one (five) for heart failure, two (four) for heart rhythm disturbances, 14 (28) for ischaemic stroke, and two (four) for haemorrhagic stroke. At the national average level, an increase of 10 μg/m³ in PM_2.5 was associated with a 0.26% (95% confidence interval 0.17% to 0.35%) increase in hospital admissions on the same day for cardiovascular disease, 0.31% (0.22% to 0.40%) for ischaemic heart disease, 0.27% (0.04% to 0.51%) for heart failure, 0.29% (0.12% to 0.46%) for heart rhythm disturbances, and 0.29% (0.18% to 0.40%) for ischaemic stroke, but not with haemorrhagic stroke (−0.02% (−0.23% to 0.19%)). The national average association of PM_2.5 with cardiovascular disease was slightly non-linear, with a sharp slope at PM_2.5 levels below 50 μg/m³, a moderate slope at 50-250 μg/m³, and a plateau at concentrations higher than 250 μg/m³. Compared with days with PM_2.5 up to 15 μg/m³, days with PM_2.5 of 15-25, 25-35, 35-75, and 75 μg/m³ or more were significantly associated with increases in cardiovascular admissions of 1.1% (0 to 2.2%), 1.9% (0.6% to 3.2%), 2.6% (1.3% to 3.9%), and 3.8% (2.1% to 5.5%), respectively.According to projections, achieving the Chinese grade 2 (35 μg/m³), Chinese grade 1 (15 μg/m³), and World Health Organization (10 μg/m³) regulatory limits for annual mean PM_2.5 concentrations would reduce the annual number of admissions for cardiovascular disease in China. Assuming causality, which should be done with caution, this reduction would translate into an estimated 36 448 (95% confidence interval 24 441 to 48 471), 85 270 (57 129 to 113 494), and 97 516 (65 320 to 129 820), respectively.Conclusions: These data suggest that in China, short term exposure to PM_2.5 is associated with increased hospital admissions for all major cardiovascular diseases except for haemorrhagic stroke, even for exposure levels not exceeding the current regulatory limits.

Epidemiological studies have provided compelling evidence of associations between ambient temperature and cardiovascular disease. However, evidence of effects of daily temperature variability on cardiovascular disease is scarce and mixed. We aimed to examine short-term associations between temperature variability and hospital admissions for cause-specific cardiovascular disease in urban China. We conducted a national time-series analysis in 184 cities in China between 2014 and 2017. Data on daily hospital admissions for ischemic heart disease, heart failure, heart rhythm disturbances, and ischemic stroke were obtained from the database of Urban Employee Basic Medical Insurance (UEBMI) including 0.28 billion enrollees. Temperature data were acquired from the China Meteorological Data Sharing Service Center. Temperature variability was calculated from the standard deviation (SD) of daily minimum and maximum temperatures over exposure days. City-specific associations between temperature variability and cardiovascular disease were examined with overdispersed Poisson models controlling for calendar time, day of the week, public holiday, and daily mean temperature and relative humidity. Random-effects meta-analyses were performed to obtain national and regional average associations. We also plotted exposure-response relationship curve using a natural cubic spline of temperature variability. There were 8.0 million hospital admissions for cardiovascular disease during the study period. At the national-average level, a 1-°C increase in temperature variability at 0–1 days (TV_0–1) was associated with a 0.44% (0.32%–0.55%), 0.31% (0.20%–0.43%), 0.48% (0.01%–0.96%), 0.34% (0.01%–0.67%), and 0.82% (0.59%–1.05%) increase in hospital admissions for cardiovascular disease, ischemic heart disease, heart failure, heart rhythm disturbances, and ischemic stroke, respectively. The estimates decreased but remained significant when controlling for ambient fine particulate matter (PM_2.5), NO₂, and SO₂ pollution. The main limitation of the present study was the unavailability of data on individual exposure to temperature variability. Our findings suggested that short-term temperature variability exposure could increase the risk of cardiovascular disease, which may provide new insights into the health effects of climate change.