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The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 μg m−3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 μg m−3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively., Graphical abstract Image, graphical abstract

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Background Antibiotic overprescribing in long-term care settings is driven by prescriber preferences and is associated with preventable harms for residents. We aimed to determine whether peer comparison audit and feedback reporting for physicians reduces antibiotic overprescribing among residents. Methods We employed a province wide, difference-in-differences study of antibiotic prescribing audit and feedback, with an embedded pragmatic randomized controlled trial (RCT) across all long-term care facilities in Ontario, Canada, in 2019. The study year included 1238 physicians caring for 96 185 residents. In total, 895 (72%) physicians received no feedback; 343 (28%) were enrolled to receive audit and feedback and randomized 1:1 to static or dynamic reports. The primary outcomes were proportion of residents initiated on an antibiotic and proportion of antibiotics prolonged beyond 7 days per quarter. Results Among all residents, between the first quarter of 2018 and last quarter of 2019, there were temporal declines in antibiotic initiation (28.4% to 21.3%) and prolonged duration (34.4% to 29.0%). Difference-in-differences analysis confirmed that feedback was associated with a greater decline in prolonged antibiotics (adjusted difference −2.65%, 95% confidence interval [CI]: −4.93 to −.28%, P = .026), but there was no significant difference in antibiotic initiation. The reduction in antibiotic durations was associated with 335 912 fewer days of treatment. The embedded RCT detected no differences in outcomes between the dynamic and static reports. Conclusions Peer comparison audit and feedback is a pragmatic intervention that can generate small relative reductions in the use of antibiotics for prolonged durations that translate to large reductions in antibiotic days of treatment across populations. Clinical Trials Registration. NCT03807466.

Background Peer comparison audit and feedback has demonstrated effectiveness in improving antibiotic prescribing practices, but only a minority of prescribers view their reports. We rigorously tested 3 behavioral nudging techniques delivered by email to improve report opening. Methods We conducted a pragmatic randomized controlled trial among Ontario long-term care prescribers enrolled in an ongoing peer comparison audit and feedback program which includes data on their antibiotic prescribing patterns. Physicians were randomized to 1 of 8 possible sequences of intervention/control allocation to 3 different behavioral email nudges: a social peer comparison nudge (January 2020), a maintenance of professional certification incentive nudge (October 2020), and a prior participation nudge (January 2021). The primary outcome was feedback report opening; the primary analysis pooled the effects of all 3 nudging interventions. Results The trial included 421 physicians caring for >28 000 residents at 450 facilities. In the pooled analysis, physicians opened only 29.6% of intervention and 23.9% of control reports (odds ratio [OR], 1.51 [95% confidence interval {CI}, 1.10–2.07], P = .011); this difference remained significant after accounting for physician characteristics and clustering (adjusted OR [aOR], 1.74 [95% CI, 1.24–2.45], P = .0014). Of individual nudging techniques, the prior participation nudge was associated with a significant increase in report opening (OR, 1.62 [95% CI, 1.06–2.47], P = .026; aOR, 2.16 [95% CI, 1.33–3.50], P = .0018). In the pooled analysis, nudges were also associated with accessing more report pages (aOR, 1.28 [95% CI, 1.14–1.43], P Conclusions Enhanced nudging strategies modestly improved report opening, but more work is needed to optimize physician engagement with audit and feedback. Clinical Trials Registration NCT04187742.

Here we use 16-year satellite and reanalysis data in combination with a multivariate regression model to investigate how aerosols affect cloud fraction (CF) over the East Coast of the United States. Cloud droplet number concentrations (Nd), cloud geometrical thickness, lower tropospheric stability, and relative humidity at 950 hPa (RH950) are identified as major cloud controlling parameters that explain 97% of the variability in CF. Nd is shown to play an important role in regulating the dependence of cloud fraction on RH950. The observed annual-mean CF shows no significant trend due to the cancelation from the opposite trends in Nd and RH950. The multivariate regression model revealed that the decline in Nd alone would lead to a about 20% relative decline in CF. Our results indicate the significant aerosol effects on CF and suggest the need to account for pollution-induced cloud changes in quantifying cloud feedback based on long-term observations.

In this study, an online transfer TSK fuzzy classifier O-T-TSK-FC is proposed for recognizing epilepsy signals. Compared with most of the existing transfer learning models, O-T-TSK-FC enjoys its merits from the following three aspects: 1) Since different patients often response to the same neuronal firing stimulation in different neural manners, the labeled data in the source domain cannot accurately represent the primary EEG data in the target domain. Therefore, we design an objective function which can integrate with subject-specific data in the target domain to induce the target predictive function. 2) A new regularization used for knowledge transfer is proposed from the perspective of error consensus, and its rationality is explained from the perspective of probability density estimation. 3) Clustering is used to partition source domains so as to reduce the computation of O-T-TSK-FC without affecting its performance. Based on the EEG signals collected from Bonn University, six different online scenarios for transfer learning are constructed. Experimental results on them show that O-T-TSK-FC performs better than benchmarking algorithms and robustly.

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

Air pollution may trigger systemic lupus erythematosus (SLE). However, few studies have investigated the associations between air pollution and complications of SLE, such as lupus nephritis (LN). In this study, multicenter longitudinal data from 13 hospitals in China, including 8552 SLE patients with 24,762 visits, were used. Based on the generalized estimating equation (GEE) model, we assessed the associations of LN occurrence with short-term exposures to different air pollutants including particulate matter with an aerodynamic diameter 2.5 μm (PM

Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m–3 for the former and R2 = 0.65 and RMSE = 15.69 µg m–3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.

Background The pathogenesis of Kawasaki disease (KD) is commonly ascribed to an exaggerated immunologic response to an unidentified environmental or infectious trigger in susceptible children. A comprehensive framework linking epidemiological data and global distribution of KD has not yet been proposed. Methods and findings Patients with KD (n = 81) were enrolled within 6 weeks of diagnosis along with control subjects (n = 87). All completed an extensive epidemiological questionnaire. Geographic localization software characterized the subjects’ neighborhood. KD incidence was compared to atmospheric biological particles counts and winds patterns. These data were used to create a comprehensive risk framework for KD, which we tested against published data on the global distribution. Compared to controls, patients with KD were more likely to be of Asian ancestry and were more likely to live in an environment with low exposure to environmental allergens. Higher atmospheric counts of biological particles other than fungus/spores were associated with a temporal reduction in incidence of KD. Finally, westerly winds were associated with increased fungal particles in the atmosphere and increased incidence of KD over the Greater Toronto Area. Our proposed framework was able to explain approximately 80% of the variation in the global distribution of KD. The main limitations of the study are that the majority of data used in this study are limited to the Canadian context and our proposed disease framework is theoretical and circumstantial rather than the result of a single simulation. Conclusions Our proposed etiologic framework incorporates the 1) proportion of population that are genetically susceptible; 2) modulation of risk, determined by habitual exposure to environmental allergens, seasonal variations of atmospheric biological particles and contact with infectious diseases; and 3) exposure to the putative trigger. Future modelling of individual risk and global distribution will be strengthened by taking into consideration all of these non-traditional elements.

Background: The etiology of Kawasaki disease (KD) is commonly described as an exaggerated immune response to an environmental or infectious trigger in developmentally, immunologically and genetically susceptible children. The trigger for KD has not yet been identified. Methods: Patients with newly diagnosed KD (n=73) were enrolled within 6 weeks of KD diagnosis. Control subjects (n=65) were enrolled during pediatric annual visits and from friends of enrolled KD patients. All participants completed an extensive questionnaire about medical history, family and environment. Geographic localisation software characterized the participants’ neighborhoods. A hypothetical scenario was used for control patients to simulate similar recall bias to that of the KD patients. Results: There were no differences in age, gender, allergies, child and family medical history or recent vaccination. Children with KD were more likely to be unwell prior to KD symptom onset than controls, including fever, stomach pain, lethargy, jaundice, loss of appetite and irritability, but not infectious disease symptoms (16% vs. 21%, p=0.49). Infectious symptomatology developed concurrently to classic KD symptoms. Children with KD had less exposure to environmental allergens and were more likely to be exposed to chemical irritants. Conclusions: The presence of symptoms for up to 1 month before onset of KD may suggest that a preceding illness/environmental agents may alter barriers to entry/exposure or prime an immunologic reaction to an etiologic trigger. Children with fewer allergen/infectious exposures and chronic chemical exposure may be immunologically more susceptible.