Author Interviews, JAMA, Opiods / 27.11.2019
Decrease in Life Expectancy Concentrated in Rust Belt and Appalachia
MedicalResearch.com Interview with:
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Dr. Woolf[/caption]
Steven H. Woolf, MD, MPH
Director Emeritus and Senior Advisor, Center on Society and Health
Professor, Department of Family Medicine and Population Health
C. Kenneth and Dianne Wright Distinguished Chair in Population Health and Health Equity
Virginia Commonwealth University School of Medicine
Richmond, Virginia 23298-0212
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Life expectancy in the US has decreased for three years in a row, the first time this has occurred in this country since the Spanish flu epidemic a century ago. Meanwhile, life expectancy in other countries continues to climb.
Our study found that the trend is being driven by an increase in death rates among working-age adults (ages 25-64 years), which began as early as the 1990s. The increase has involved deaths from drug overdoses—a major contributor—but also from alcoholism, suicides, and a long list of organ diseases. We found increases in 35 causes of death.
We analyzed the trends across the 50 states and discovered that the trend is concentrated in certain regions, especially the Industrial Midwest (Rust Belt) and Appalachia, whereas other regions like the Pacific states were least affected. Increases in midlife mortality in four Ohio Valley states (Ohio, Pennsylvania, Indiana and Kentucky) accounted for one third of the excess deaths between 2010 and 2017.
Dr. Woolf[/caption]
Steven H. Woolf, MD, MPH
Director Emeritus and Senior Advisor, Center on Society and Health
Professor, Department of Family Medicine and Population Health
C. Kenneth and Dianne Wright Distinguished Chair in Population Health and Health Equity
Virginia Commonwealth University School of Medicine
Richmond, Virginia 23298-0212
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Life expectancy in the US has decreased for three years in a row, the first time this has occurred in this country since the Spanish flu epidemic a century ago. Meanwhile, life expectancy in other countries continues to climb.
Our study found that the trend is being driven by an increase in death rates among working-age adults (ages 25-64 years), which began as early as the 1990s. The increase has involved deaths from drug overdoses—a major contributor—but also from alcoholism, suicides, and a long list of organ diseases. We found increases in 35 causes of death.
We analyzed the trends across the 50 states and discovered that the trend is concentrated in certain regions, especially the Industrial Midwest (Rust Belt) and Appalachia, whereas other regions like the Pacific states were least affected. Increases in midlife mortality in four Ohio Valley states (Ohio, Pennsylvania, Indiana and Kentucky) accounted for one third of the excess deaths between 2010 and 2017.
Dr. Villanti[/caption]
Andrea Villanti, PhD, MPH
Associate Professor
Department of Psychiatry
Vermont Center on Behavior and Health
University of Vermont
MedicalResearch.com: What is the background for this study?
Response: Our earlier work documented a significant association between first use of a flavored tobacco product and current tobacco use (
Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.
