Addiction, Author Interviews, Columbia, JAMA / 05.02.2021

MedicalResearch.com Interview with: Elodie C. Warren, MPH Columbia University Mailman School of Public Health Graduate MedicalResearch.com: What is the background for this study? What are the main findings? Response: We know that the US has been experiencing an opioid crisis for the past two decades. And we know that among communities of color, rates of overdose deaths are continuing to increase, even though overall national rates decreased between 2017 and 2018. To better understand how the opioid crisis has differently affected racial/ethnic groups, we looked at how heroin treatment admissions changed over time by race/ethnicity, age, and sex. We found that there were stark differences when comparing non-Hispanic Black men and women to non-Hispanic White men and women. Importantly, our study suggests the existence of an aging cohort of Black men and women (likely including survivors of a heroin epidemic that hit urban areas more than 40 years ago) that continues to struggle with heroin addiction. This points to the need for targeted interventions in chronically underserved communities.  (more…)
Author Interviews, Columbia, COVID -19 Coronavirus, NYU, Technology / 02.04.2020

MedicalResearch.com Interview with: Professor Anasse Bari PhD Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, and Megan Coffee MD PhD Division of Infectious Diseases and Immunology, Department of Medicine New York University, Department of Population and Family Health Mailman School of Public Health Columbia University, New York MedicalResearch.com: What is the background for this study? Coffee and Bari:  This work is led by NYU Grossman School of Medicine and NYU’s Courant Institute of Mathematical Sciences, in partnership with Wenzhou Central Hospital and Cangnan People's Hospital, both in Wenzhou, China. This is a multi-disciplinary team with backgrounds in clinical infectious disease as well as artificial intelligence (AI) and computer science. There is a critical need to better understand COVID-19. Doctors learn from collective and individual clinical experiences. Here, no clinician has years of experience. All are learning as they go, having to make important decisions about clinical management with stretched resources. The goal here is to augment clinical learning with machine learning. In particular, the goal is to allow clinicians to identify early who from the many infected will need close medical attention. Most patients will first develop mild symptoms, yet some 5-8 days later will develop critical illness. It is hard to know who these people are who will need to be admitted and may need to be intubated until they become ill. Knowing this earlier would allow more attention and resources to be spent on those patients with worse prognoses. If there were ever treatments in the future that could be used early in the course of illness, it would be important to identify who would most benefit We present in this study a first step in building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. It is at this point a proof of concept that it could be possible to identify future severity based on initial presentation in COVID-19. (more…)
Author Interviews, Columbia, Infections / 30.10.2019

MedicalResearch.com Interview with: Barun Mathema PhD Assistant Professor,Epidemiology Mailman School of Public Health Columbia University MedicalResearch.com: What is the background for this study? Response: In 2005 a major outbreak of extensively drug-resistant tuberculosis (XDR-TB) causing over 90% mortality was reported in rural town of Tugela Ferry, KwaZulu-Natal, South Africa. The strain that caused the outbreak was resistant to all first and most second line antibiotics. This strain has since been recovered throughout the district and accounts for over 79% of all XDR-TB. We were interested in understanding the basic epidemiological and evolutionary forces that enabled this strain to proliferate. More simply, when and where this strain emerged, and how and why it became dominant.  (more…)