ABSTRACT
BACKGROUND AND AIM: The COVID-19 pandemic has been associated with an increase in the frequency and severity of new-onset diabetic ketoacidosis (DKA) in children (<18). We aimed to compare the incidence of life-threatening DKA complications among patients admitted to our pediatric intensive care unit (PICU) with new-onset DKA presenting pre- and post-the COVID-19 pandemic. METHOD(S): Retrospective observational study of children (0-18 years) admitted to our PICU with a diagnosis of newonset DKA between March 2018 and December 2020. Life-threatening DKA complications were defined as cerebral edema (defined as requiring hyperosmolar therapy and/ or cerebral imaging), respiratory failure requiring invasive mechanical ventilation, shock or hypotension requiring vasopressors, cardiac arrhythmias, cardiac arrest or death. Data was obtained by review of medical records. Analysis was performed using Chi-squared test, and Fisher's exact test where appropriate. RESULT(S): A total of 274 pediatric patients were admitted to our PICU with new-onset DKA between March 2018 and December 2020. There was a total of 12 life-threatening DKA complications among the pre-pandemic cohort (n=157) who presented between March 2018-February 2020. In comparison, there were 27 life-threatening complications among the post-pandemic cohort (n=117) who presented between March-December 2020. The difference was statistically significant (p= 0.0018). However, when analyzed individually, differences among cerebral edema (p=0.066), respiratory failure requiring mechanical ventilation (p=0.77) and shock (p=0.17) failed to reach statistical significance. This is likely due to the low overall incidence in which these individual complications occur. CONCLUSION(S): Following the COVID-19 pandemic we observed an overall increase in the incidence of lifethreatening DKA complications among pediatric ICU patients.
ABSTRACT
This paper reports on Data Analytics Research (DAR), a course-based undergraduate research experience (CURE) in which undergraduate students conduct data analysis research on open real-world problems for industry, university, and community clients. We describe how DAR, offered by the Mathematical Sciences Department at Rensselaer Polytechnic Institute (RPI), is an essential part of an early low-barrier pipeline into data analytics studies and careers for diverse students. Students first take a foundational course, typically Introduction to Data Mathematics, that teaches linear algebra, data analytics, and R programming simultaneously using a project-based learning (PBL) approach. Then in DAR, students work in teams on open applied data analytics research problems provided by the clients. We describe the DAR organization which is inspired in part by agile software development practices. Students meet for coaching sessions with instructors multiple times a week and present to clients frequently. In a fully remote format during the pandemic, the students continued to be highly successful and engaged in COVID-19 research producing significant results as indicated by deployed online applications, refereed papers, and conference presentations. Formal evaluation shows that the pipeline of the single on-ramp course followed by DAR addressing real-world problems with societal benefits is highly effective at developing students' data analytics skills, advancing creative problem solvers who can work both independently and in teams, and attracting students to further studies and careers in data science.
ABSTRACT
In this exploratory study, we scrutinize a database of over one million tweets collected from March to July 2020 to illustrate public attitudes towards mask usage during the COVID-19 pandemic. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for each theme using automatic text summarization. In recent months, a body of literature has highlighted the robustness of trends in online activity as proxies for the sociological impact of COVID-19. We find that topic clustering based on mask-related Twitter data offers revealing insights into societal perceptions of COVID- 19 and techniques for its prevention. We observe that the volume and polarity of mask-related tweets has greatly increased. Importantly, the analysis pipeline presented may be leveraged by the health community for qualitative assessment of public response to health intervention techniques in real time.
ABSTRACT
This study examines how social determinants associated with COVID-19 mortality change over time. Using US county-level data from July 5 and December 28, 2020, the effect of 19 high-risk factors on COVID-19 mortality rate was quantified at each time point with negative binomial mixed models. Then, these high-risk factors were used as controls in two association studies between 40 social determinants and COVID-19 mortality rates using data from the same time points. The results indicate that counties with certain ethnic minorities and age groups, immigrants, prevalence of diseases like pediatric asthma and diabetes and cardiovascular disease, socioeconomic inequalities, and higher social association are associated with increased COVID-19 mortality rates. Meanwhile, more mental health providers, access to exercise, higher income, chronic lung disease in adults, suicide, and excessive drinking are associated with decreased mortality. Our temporal analysis also reveals a possible decreasing impact of socioeconomic disadvantage and air quality, and an increasing effect of factors like age, which suggests that public health policies may have been effective in protecting disadvantaged populations over time or that analysis utilizing earlier data may have exaggerated certain effects. Overall, we continue to recognize that social inequality still places disadvantaged groups at risk, and we identify possible relationships between lung disease, mental health, and COVID-19 that need to be explored on a clinical level. © 2021 ACM.
ABSTRACT
INTRODUCTION: We evaluated the use of text messages to communicate information to patients whose surgeries were postponed because of the COVID-19 restriction on elective surgeries. Our hypothesis was that text messaging would be an effective way to convey updates. METHODS: In this observational study, 295 patients received text messaging alerts. Eligibility included patients who had their surgery postponed and had a cell phone that received text messages. Engagement rates were determined using embedded smart links. Patient survey responses were collected. RESULTS: A total of 3,032 texts were delivered. Engagement rates averaged 90%. Survey responses (n = 111) demonstrated that 98.2% of patients liked the text messages and 95.5% said that they felt more connected to their care team;91.9% of patients agreed that the text updates helped them avoid calling the office. Patients with higher pain levels reported more frustration with their surgery delay (5.3 versus 2.8 on 1 to 10 scale, P value < 0.01). More frustrated patients wished they received more text messages (24.4% versus 4.6%, P value = 0.04) and found the content less helpful (8.2 versus 9.2 on 1 to 10 scale, P value = 0.01). CONCLUSION: Text messaging updates are an efficient way to communicate with patients during the COVID-19 pandemic.