Health disparities among pregnant women diagnosed with COVID-19 in Philadelphia

Introduction: The CDC has cited language barriers and racial discrimination as some of the social determinants of health during the COVID-19 pandemic. This study aims to investigate the socioeconomic factors that affect COVID-19 diagnosis and outcomes in pregnant women. We hypothesize that women whose primary language is not English will have higher rates of COVID-19 compared to women whose primary language is English. Methods: This is a retrospective cohort study of women who delivered at TJUH between 04/13/2020 and 06/31/2020. Data on demographics, SARS-CoV-2 PCR, maternal, fetal, neonatal outcomes were collected. The primary outcome was the proportion of English vs Non-English-speaking patients with and without SARS-CoV-2 positive PCR. Data were analyzed using a Chi-squared test. Multivariable logistic regression will be used to control for the effect of factors including comorbidities and income level. The study was approved by TJUH Institutional Review Board. Results: Preliminary data are herein reported. 473 women have been included thus far (of 713 eligible), 106 tested positive and 367 tested negative. Overall, the preferred language was English in 78.4%, Spanish in 12.9%, Other in 8.7%. There were significantly more Non-English-speaking patients in the COVID-19 positive group than in the COVID-19 negative group (36.8% vs 17.2%, p\u3c0.001). Discussion: Non-English-speaking pregnant women are disproportionally represented in the COVID positive patient population, which supports our hypothesis. This suggest that language is significant barrier to SARS-CoV-2 care, this may be related to other sociodemographic factors. Further analysis will provide data on the impact of this disparity. Data collection will be completed in January 2021

Detection of Malpractice in E-exams by Head Pose and Gaze Estimation

Examination malpractice is a deliberate wrong doing contrary to official examina-tion rules designed to place a candidate at unfair advantage or disadvantage. The proposed system depicts a new use of technology to identify malpractice in E-Exams which is essential due to growth of online education. The current solu-tions for such a problem either require complete manual labor or have various vulnerabilities that can be exploited by an examinee. The proposed application en-compasses an end-to-end system that assists an examiner/evaluator in deciding whether a student passes an online exam without any probable attempts of mal-practice or cheating in e-exams with the help of visual aids. The system works by categorizing the student’s VFOA (visual focus of attention) data by capturing the head pose estimates and eye gaze estimates using state-of-the-art machine learn-ing techniques. The system only requires the student (test-taker) to have a func-tioning internet connection along with a webcam to transmit the feed. The exam-iner is alerted when the student wavers in his VFOA, from the screen greater than X, a predefined threshold of times. If this threshold X is crossed, the appli-cation will save the data of the person when his VFOA is off the screen and send it to the examiner to be manually checked and marked whether the action per-formed by the student was an attempt at malpractice or just momentary lapse in concentration. The system use a hybrid classifier approach where two different classifiers are used, one when gaze values are being read successfully (which may fail due to various reasons like transmission quality or glare from his specta-cles), the model falls back to the default classifier which only reads the head pose values to classify the attention metric, which is used to map the student’s VFOA to check the likelihood of malpractice. The model has achieved an accuracy of 96.04 percent in classifying the attention metric