Changes in interferon-γ release assay readout after COVID-19 vaccination: A prospective cohort study.

OBJECTIVES: Interferon-γ release assays (IGRAs) are widely used in public health practice to diagnose latent tuberculosis. During the COVID-19 pandemic and rollout of COVID-19 vaccination, it has remained unclear whether COVID-19 vaccines interfere with IGRA readouts. METHODS: We prospectively recruited healthcare workers during their annual occupational health examinations in 2021. Baseline IGRA readouts were compared with follow-up data after the participants had received two doses of COVID-19 vaccination. RESULTS: A total of 134 baseline IGRA-negative cases (92 with ChAdOx1 vaccine, 27 with mRNA-1273 vaccine, and 15 with heterologous vaccination) and seven baseline IGRA-positive cases were analyzed. Among the baseline IGRA-negative cases, there were decreased interferon-γ concentrations over the Nil (P = 0.005) and increased Mitogen-Nil (P < 0.001) values after vaccination. For TB2-Nil value, a similar trend (P = 0.057) of increase was observed. Compared with the 0.35 IU/ml threshold, the baseline and follow-up readout differences were less than |± 0.10| IU/ml over the TB1-Nil and TB2-Nil values in >90% baseline IGRA-negative cases. No significant readout difference was observed among baseline IGRA-positive cases. CONCLUSION: COVID-19 vaccination did not change IGRA interpretation in most cases. Cases showing conversion/borderline IGRA readouts should be given special consideration.

Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19

This paper employs data envelopment analysis (DEA) to determine crop production efficiency in 15 major provinces of China during 2019-2020. The total power of agricultural machinery, the application amount of chemical fertilizer, the irrigation area of cultivated land, the area of grain sowing, and the total capacity of reservoirs in each province are defined as the input items. The production of food, production of oil plants, and production of fruits are considered output items. According to the findings from the DEA, the most efficient crop production is observed in Shandong and Xinjiang provinces. We also discuss the role of farmers' uncertainty perceptions in COVID-19. By cluster analysis, the provinces with large grain sown area and high grain yield are Henan and Heilongjiang, the provinces with moderate grain production in the grain sown area are Hunan, Hubei, Jiangxi, Guizhou, and Yunnan, and Xinjiang, Shandong, Hebei, Anhui, Sichuan, Jiangsu, Inner Mongolia, and Jilin are the provinces with low grain production. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Multi-ethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes (preprint)

Background: Disparate COVID-19 outcomes have been observed between Hispanic, Non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood. Methods: : This was a retrospective study utilizing electronic medical record data from five hospitals within a single academic health system based in New York City. Multivariable logistic regression models were used to identify demographic, clinical, and lab values associated with in-hospital mortality. Results: : 3,086 adult patients with self-reported race/ethnicity information presenting to the emergency department and hospitalized with COVID-19 up to April 13, 2020 were included in this study. While older age (multivariable OR 1.06, 95% CI 1.05-1.07) and baseline hypoxia (multivariable OR 2.71, 95% CI 2.17-3.36) were associated with increased mortality overall and across all races/ethnicities, Non-Hispanic Black (median age 67, IQR 58-76) and Hispanic (median age 63, IQR 50-74) patients were younger and had different comorbidity profiles compared to Non-Hispanic White patients (median age 73, IQR 62-84; p<0.05 for both comparisons). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between the Non-Hispanic Black population and interleukin-1-beta (interaction p-value 0.04). Conclusions: : This analysis of a multi-ethnic cohort highlights the need for inclusion and consideration of diverse popualtions in ongoing COVID-19 trials targeting inflammatory cytokines.

Multi-ethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes (preprint)

Objectives: To compare risk factors for COVID-19 mortality among hospitalized Hispanic, Non-Hispanic Black, and White patients. Design: Retrospecitve cohort study Setting: Five hosptials within a single academic health system Participants: 3,086 adult patients with self-reported race/ethnicity information presenting to the emergency department and hospitalized with COVID-19 up to April 13, 2020. Main outcome measures: In-hospital mortality Results: While older age (multivariable OR 1.06, 95% CI 1.05-1.07) and baseline hypoxia (multivariable OR 2.71, 95% CI 2.17-3.36) were associated with increased mortality overall and across all races/ethnicities, Non-Hispanic Black (median age 67, IQR 58-76) and Hispanic (median age 63, IQR 50-74) patients were younger and had different comorbidity profiles compared to Non-Hispanic White patients (median age 73, IQR 62-84; p<0.05 for both comparisons). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between the Non-Hispanic Black population and interleukin-1-beta (interaction p-value 0.04). Conclusions: This analysis of a multi-ethnic cohort highlights the need for inclusion and consideration of diverse popualtions in ongoing COVID-19 trials targeting inflammatory cytokines.

Modeling the transmission of the SARS-CoV-2 delta variant in a partially vaccinated population (preprint)

In a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that considers both subpopulations and immunity parameters including vaccination rates, vaccine effectiveness, and a gradual loss of protection. The model forecasted the spread of the SARS-CoV-2 delta variant in the US under varied transmission and vaccination rates. We further obtained the control reproduction number and conducted sensitivity analyses to determine how each parameter may affect virus transmission. Our results show that a combination of strengthening vaccine-induced immunity and preventative behavioral measures will likely be required to deaccelerate the rise of infectious SARS-CoV-2 variants. One-Sentence Summary Mathematical models considering vaccinated and unvaccinated individuals help forecast and manage the spread of new SARS-CoV-2 variants.

Prevalence and Risk Factors Associated With Self-reported Psychological Distress Among Children and Adolescents During the COVID-19 Pandemic in China.

Importance: Schools have been suspended nationwide in 188 countries, and classes have shifted to home-based distance learning models to control the spread of the coronavirus disease 2019 (COVID-19) pandemic. Additional information is needed to determine mental health status among school-aged children and adolescents during this public health crisis and the risk factors associated with psychological distress during the pandemic. Objective: To assess self-reported psychological distress among school-aged children and adolescents associated with the COVID-19 pandemic. Design, Setting, and Participants: This cross-sectional study using data from a survey on the mental health of school-aged children and adolescents in Guangdong province, China, conducted by using a stratified cluster random sampling method between March 8 to 30, 2020. To estimate outcomes associated with location of districts, only data from students with internet protocol addresses and current addresses in Guangdong were included. Data were analyzed from April 5 to July 20, 2020. Exposure: Home-based distance learning during the COVID-19 pandemic. Main Outcome and Measures: The main outcome was self-reported psychological distress, measured using the total score on the 12-item General Health Questionnaire of 3 or greater. Multivariate logistic regression was used to analyze risk factors associated with mental health status. Odds ratios (ORs) were used to analyze the associations of factors with psychological distress. Results: Among 1 310 600 students who completed the survey, 1 199 320 students (mean [SD] age, 12.04 [3.01] years; 619 144 [51.6%] boys) were included in the final analysis. A total of 126 355 students (10.5%) self-reported psychological distress. Compared with students in primary school, high school students had increased risk of psychological distress (OR, 1.19 [95% CI, 1.15-1.23]). Compared with students who wore a face mask frequently, students who never wore a face mask had increased risk of psychological distress (OR, 2.59 [95% CI, 2.41-2.79]). Additionally, students who spent less than 0.5 hours exercising had increased odds of self-reported psychological distress compared with students who spent more than 1 hour exercising (OR, 1.64 [95% CI, 1.61-1.67]). Conclusions and Relevance: These findings suggest that the prevalence of self-reported psychological distress among students during the COVID-19 pandemic was relatively high. Frequency of wearing a face mask and time spent exercising were factors associated with mental health. Therefore, it may be necessary for governments, schools, and families to pay attention to the mental health of school-aged children and adolescents during the COVID-19 pandemic and take corresponding countermeasures to reduce the impact of the COVID-19 pandemic on students' mental health.

Prediction of COVID-19 status using baseline demographics and lab data (preprint)

The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting COVID-19 positive test status relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 status prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.

Prediction of COVID-19 Status Using Baseline Demographics and Lab Data (preprint)

Background: The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings.Methods: In this study, we developed a machine learning-based framework for predicting COVID-19 positive test status relying only on readily available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we divided the patient data into a training set (patient encounters through April 13, 2020) and a test set (patient encounters from April 13, 2020 through June 2, 2020). We trained our machine learning models on the training set and evaluated model performance on the test set.Findings: We trained and tested multiple types of machine learning models, achieving an area under the curve of 0·75 in the test set. Feature importance analyses highlighted serum calcium levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we identified an optimal probability threshold for patient screening and developed a single decision tree model that provided an operable method for stratifying sub-populations.Interpretation: Overall, this study provides a proof-of-concept that COVID-19 status prediction models can be developed using only baseline data. Our machine learning models can be adapted to a variety of global pandemic scenarios, as the resulting prediction could complement existing tests to enhance screening and pandemic containment workflows.Funding: Icahn School of Medicine at Mount Sinai, New York, NY.Declaration of Interests: J.F. is an employ of Outco Inc. All other authors declare no competing financial interests.Ethics Approval Statement: This study utilized de-identified data extracted from the electronic health record and as such was considered nonhuman subject research. Therefore, this study was exempted from the Mount Sinai IRB review and approval process. All analyses were carried out in accordance with relevant guidelines and regulations.

Mortality and risk factors among US Black, Hispanic, and White patients with COVID-19 (preprint)

Background: Little is known about risk factors for COVID-19 outcomes, particularly across diverse racial and ethnic populations in the United States. Methods: In this prospective cohort study, we followed 3,086 COVID-19 patients hospitalized on or before April 13, 2020 within an academic health system in New York (The Mount Sinai Health System) until June 2, 2020. Multivariable logistic regression was used to evaluate demographic, clinical, and laboratory factors as independent predictors of in-hospital mortality. The analysis was stratified by self-reported race and ethnicity. Findings: A total of 3,086 COVID-19 patients were hospitalized, of whom 680 were excluded (78 due to missing race or ethnicity data, 144 were Asian, and 458 were of other unspecified race/ethnicity). Of the 2,406 patients included, 892 (37.1%) were Hispanic, 825 (34.3%) were black, and 689 (28.6%) were white. Black and Hispanic patients were younger than White patients (median age 67 and 63 vs. 73, p<0.001 for both), and they had different comorbidity profiles. Older age and baseline hypoxia were associated with increased mortality across all races. There were suggestive but non-significant interactions between Black race and diabetes (p=0.09), and obesity (p=0.10). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between Black race and interleukin-1-beta (p=0.04), and a suggestive interactions between Hispanic ethnicity and procalcitonin (p=0.07) and interleukin-8 (p=0.09). Interpretation: In this large, racially and ethnically diverse cohort of COVID-19 patients in New York City, we identified similarities and important differences across racial and ethnic groups in risk factors for in-hospital mortality.

Temporal variations in the air quality index and the impact of the COVID-19 event on air quality in Western China

This study investigated the AQI (air quality index) and atmospheric pollutants including PM 2.5, PM 10, CO, SO 2, NO 2and O 3in Chongqing, Luzhou and Chengdu from 2017 to 2019. In addition, the impacts of the COVID-19 event on the air quality in the three cities in 2020 were compared and discussed. For the combined AQIs for the three cities, in spring, the daily AQIs ranged between 25 and 182 and averaged 72.1. In summer, the daily AQIs ranged between 24 and 206 and averaged 77.5. In autumn, the daily AQIs ranged between 22 and 170 and averaged 61.1, and in winter, the daily AQIs ranged between 28 and 375 and averaged 99.6. The distributions of the six AQI classes in spring were 3%, 94%, 3%, 0%, 0%, and 0%;in summer, they were 11%, 74%, 15%, 0%, 0% and 0%;in autumn, they were 29%, 70%, 1%, 0%, 0%, and 0%, and in winter, they were 1%, 52%, 44%, 3%, 0%, and 0%, respectively. The average AQIs, in order, were Chengdu (85.4) > Chongqing (73.8) > Luzhou (73.2). Both the highest AQIs and PM 2.5(as the major indicatory air pollutant) occurred mainly in the low temperature season (January, December, and February), while O 3was the main air pollutant in June and August when the weather was hot. In February 2020, during the epidemic prevention and control actions taken in response to COVID-19 for the three cities, the combined AQIs for the top five days with the highest AQIs in February 2020 was 79.4, which was 23.6% lower than that from 2017–2019 (AQI = 100.7), and the average concentrations of PM 2.5, PM 10, SO 2, CO, and NO 2were 89.4 µg m –3, 106 µg m –3, 2.31 ppb, 0.72 ppm, and 12.3 ppb, respectively, and were 17.9%, 30.8%, 83.8%, 19.8%, and 62.1%, lower than those in February 2017–2019. However, the average O 3concentration (31.8 ppb) in February 2020 rather than decreasing, increased by 6.2%. This is because a lower NO 2concentration hindered the NO + O 3reaction and led to increase O 3concentration in the ambient air.

Impact of the covid-19 event on trip intensity and air quality in southern china

The COVID-19 epidemic discovered and reported at the end of December 2019 and began spreading rapidly around the world. The impact of the COVID-19 event on the trip intensity, AQI (air quality index), and air pollutants, including PM2.5, PM10, SO2, CO, NO2, and O3in Shenzhen, Guangzhou, and Foshan (the so-called ‘three cities’) from January 12 to March 27, in 2019 and 2020, are compared and discussed. In 2020, the combined trip intensity in the three cities ranged between 0.73 and 5.54 and averaged 2.57, which was 28.4% lower than that in 2019. In terms of the combined AQIs for the three cities, from January 12 to March 26, 2020, the daily AQIs ranged between 21.0 and 121.3 and averaged 56.4, which was 16.0% lower than that in 2019. The average AQIs in order were Guangzhou (57.5) > Foshan (54.1) > Shenzhen (44.1). In 2019, the distribution proportions of the six AQI classes were 45.2%, 50.4%, 4.40%, 0%, 0%, and 0%, respectively, while those in 2020 were 62.7%, 37.3%, 0%, 0%, 0% and 0%, respectively. For the combined data for the three cities, on the top five days with the highest AQIs during the epidemic period, the average concentrations of PM2.5, PM10, SO2, CO, NO2, and O3were 76.4 µg m–3, 113.4 µg m–3, 5.14 ppb, 0.88 ppm, 36.5 ppb and 55.5 ppb, which were 55.2%, 49.4%, 55.1%, 30.0%, 45.1% and 15.5% lower than those during the non-epidemic period (from January 12 to March 27, 2017–2019). The above results revealed that the comprehensive strict epidemic prevention and control actions reduced trip intensity and improved the air quality significantly.

Sex-specificity of mortality risk factors among hospitalized COVID-19 patients in New York City: prospective cohort study (preprint)

Objective: To identify sex-specific effects of risk factors for in-hospital mortality among COVID-19 patients admitted to a hospital system in New York City. Design: Prospective observational cohort study with in-hospital mortality as the primary outcome. Setting: Five acute care hospitals within a single academic medical system in New York City. Participants: 3,086 hospital inpatients with COVID-19 admitted on or before April 13, 2020 and followed through June 2, 2020. Follow-up till discharge or death was complete for 99.3% of the cohort. Results: The majority of the cohort was male (59.6%). Men were younger (median 64 vs. 70, p<0.001) and less likely to have comorbidities such as hypertension (32.5% vs. 39.9%, p<0.001), diabetes (22.6% vs. 26%, p=0.03), and obesity (6.9% vs. 9.8%, p=0.004) compared to women. Women had lower median values of laboratory markers associated with inflammation compared to men: white blood cells (5.95 vs. 6.8 K/uL, p<0.001), procalcitonin (0.14 vs 0.21 ng/mL, p<0.001), lactate dehydrogenase (375 vs. 428 U/L, p<0.001), C-reactive protein (87.7 vs. 123.2 mg/L, p<0.001). Unadjusted mortality was similar between men and women (28.8% vs. 28.5%, p=0.84), but more men required intensive care than women (25.2% vs. 19%, p<0.001). Male sex was an independent risk factor for mortality (OR 1.26, 95% 1.04-1.51) after adjustment for demographics, comorbidities, and baseline hypoxia. There were significant interactions between sex and coronary artery disease (p=0.038), obesity (p=0.01), baseline hypoxia (p<0.001), ferritin (p=0.002), lactate dehydrogenase (p=0.003), and procalcitonin (p=0.03). Except for procalcitonin, which had the opposite association, each of these factors was associated with disproportionately higher mortality among women. Conclusions: Male sex was an independent predictor of mortality, consistent with prior studies. Notably, there were significant sex-specific interactions which indicated a disproportionate increase in mortality among women with coronary artery disease, obesity, and hypoxia. These new findings highlight patient subgroups for further study and help explain the recognized sex differences in COVID-19 outcomes.

Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin (preprint)

Since the SARS outbreak 18 years ago, a large number of severe acute respiratory syndrome related coronaviruses (SARSr-CoV) have been discovered in their natural reservoir host, bats1-4. Previous studies indicated that some of those bat SARSr-CoVs have the potential to infect humans5-7. Here we report the identification and characterization of a novel coronavirus (nCoV-2019) which caused an epidemic of acute respiratory syndrome in humans, in Wuhan, China. The epidemic, started from December 12th, 2019, has caused 198 laboratory confirmed infections with three fatal cases by January 20th, 2020. Full-length genome sequences were obtained from five patients at the early stage of the outbreak. They are almost identical to each other and share 79.5% sequence identify to SARS-CoV. Furthermore, it was found that nCoV-2019 is 96% identical at the whole genome level to a bat coronavirus. The pairwise protein sequence analysis of seven conserved non-structural proteins show that this virus belongs to the species of SARSr-CoV. The nCoV-2019 virus was then isolated from the bronchoalveolar lavage fluid of a critically ill patient, which can be neutralized by sera from several patients. Importantly, we have confirmed that this novel CoV uses the same cell entry receptor, ACE2, as SARS-CoV.