Rebecca Myerson is an Assistant Professor at the University of Southern California School of Pharmacy in the Department of Pharmaceutical and Health Economics. She is a member of our Health Inequality network and an alumnus of our Summer School on Socioeconomic Inequality. Her research interests include policy evaluation, health economics, and health services research. In particular, her work focuses on how interventions and policy changes affect incidence, diagnosis, and treatment of non-communicable disease.
Describe your area of study and how it relates to current policy discussions surrounding inequality.
I study how different patients receive health care in different policy contexts. Which I think is a critical issue in health policy now because of the millions of people who are entering or re-entering primary care as a result of health insurance expansions. In addition to evaluating which policies help to increase health care uptake, I am interested in this from a metrics perspective: How do changes in the composition of patients shape the observed impacts of policy? In much of my current work, I collaborate with epidemiologists and physicians. I think there’s a nice synergy between an epidemiological approach of tracking patient-level inputs and health outcomes, and economic questions about what how patients’ information sets evolve and how they self-select into health care, health behaviors, and health insurance.
My current focus is on patients who were undiagnosed or untreated for diabetes, high cholesterol, and hypertension, but became screened and treated due to incentives or policy changes. I focus on those because those conditions can be asymptomatic and are top contributors to premature death and preventable burden of disease in the United States. They contribute to geographic inequality in health across the United States – think of the Stroke Belt – as well as gaps in life expectancy by income, sex, and race. Research from IHME has found that gaps in life expectancy by geography in the United States are expanding and that in some counties, women actually have a shorter life expectancy than they did two decades ago. So I think changes in the composition of patients and in diagnosis of these patients is a critical issue.
What areas in the study of inequality are most in need of new research?
My read of the evidence is that addressing information barriers, not just cost barriers, will be essential in creating a health insurance marketplace that works for everyone. The Health Reform Monitoring Survey found that over half of the people who were uninsured prior to the Affordable Care Act Health Insurance Marketplaces and Medicaid expansions did not understand key health insurance concepts. Funding to assist consumers in signing up and selecting plans has decreased, particularly funding for the Consumer Assistance Program grants, which supported programs to help people to resolve issues with their insurance after they enroll. Given this context, I think that models of health insurance markets that consider health literacy as one determinant of health plan selection and dropout are very policy relevant.
I think another great area for research in health inequality is design of health care quality metrics. Public reporting has appeal to improve the transparency and quality of the health care system, but researchers have found that some quality metrics can have unintended consequences related to patient selection. There is a difficult balance to creating metrics that are easy to use and encourage appropriate health care without penalizing providers who diagnose and treat vulnerable patients.
What advice do you have for emerging scholars in your field?
I echo the advice of others here – I recommend reading papers from multiple fields including health economics, epidemiology, medicine, and public health, and collaborating with people from multiple fields. I think that if our work can reach a diverse audience, it will have more impact.
Every empiricist is concerned about selecting the right data, so I’d also raise the issue that patients with low health care access can be hard to track in some data sources. For example, I study late diagnosis as a source of health disparities, so I was concerned that some people who are undiagnosed for their conditions can be hard to track in claims data. Sometimes you may face trade-offs between leveraging big data and being able to dig into particular issues underlying health inequality.