Author Interviews, Health Care Systems, JAMA / 22.01.2021
Disparities in Physician Density by Specialty Type in Urban and Rural Counties
MedicalResearch.com Interview with:
[caption id="attachment_56455" align="alignleft" width="200"] Dr. Machado[/caption]
Sara Machado PhD
Fellow at the Department of Health Policy
London School of Economics and Political Science
MedicalResearch.com: What is the background for this study?
Response: Physician distribution is a determinant of health care access, so knowing how physician density patterns evolve over time is important if we are trying to address disparities in access to care. Moreover, the last 10 years have brought about changes in health care coverage, across the US. Recent evidence points to an uneven physician distribution between urban and rural communities. We examined recent trends in physician density by physician category across rural and urban US counties.
MedicalResearch.com: What are the main findings?
Response: We have two main findings.
Dr. Machado[/caption]
Sara Machado PhD
Fellow at the Department of Health Policy
London School of Economics and Political Science
MedicalResearch.com: What is the background for this study?
Response: Physician distribution is a determinant of health care access, so knowing how physician density patterns evolve over time is important if we are trying to address disparities in access to care. Moreover, the last 10 years have brought about changes in health care coverage, across the US. Recent evidence points to an uneven physician distribution between urban and rural communities. We examined recent trends in physician density by physician category across rural and urban US counties.
MedicalResearch.com: What are the main findings?
Response: We have two main findings.
- First, density of primary care physicians steadily decreased in more than half of rural counties (994 out of 1,976).
- Second, medical specialist density, which would care for cardiovascular and pulmonary disease, for example, has been largely stagnant in rural counties, at the lowest density levels (less than 10 physicians per 100,000), and increasing in metropolitan counties.
Prof. Woloshin[/caption]
Steven Woloshin, MD, MS
Professor of Medicine and Community and Family Medicine
Professor, The Dartmouth Institute for Health Policy and Clinical Practice
MedicalResearch.com: What is the background for this study?
Response: Industry spends more on detailing visits and free samples than any other form of prescription drug marketing. There is good evidence that these activities can lead to more use of expensive new drugs over equally effective cheaper options. Given these concerns there have been efforts by some hospitalls and practices to restrict these forms of marketing.
We asked physicians in group practices delivering primary care about how often pharmaceutical reps visit their practice and whether they have a free sample closet. 
Dr. Hongying (Daisy) Dai[/caption]
Hongying (Daisy) Dai, PhD
Associate Professor
Department of Biostatistics | College of Public Health
University of Nebraska Medical Center
MedicalResearch.com: What is the background for this study?
Response: Although marijuana is still classified as a Schedule I drug at the Federal level, as of June 2019, 33 states and the District of Columbia have legalized one or more forms of marijuana; 11 states and the District of Columbia have approved both medical and recreational uses. Public opinion on marijuana has changed dramatically over the last two decades and support for legalization has doubled since 2010. However, very little is known about the prevalence and patterns of marijuana use among adults with medical conditions.
This study analyzed the 2016 and 2017 Behavioral Risk Factor Surveillance System data to report the prevalence and patterns of marijuana use among adults with self-reported medical conditions. 
Jasleen Grewal, BSc.
Genome Sciences Centre
British Columbia Cancer Research Centre
Vancouver, British Columbia, Canada
MedicalResearch.com: What is the background for this study?
Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain. We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy.
Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues. 
