Health
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Justice, Equity, Diversity, & Inclusion Committee

SHP’s inaugural Health Equity Panel will take place on Friday, October 29, 2021 from 12pm – 1:15pm. The panel is a central event in the launch of the new Department of Health Policy at Stanford and will also serve to introduce our new flagship seminar series on health equity. We will convene the first panel via Zoom, but intend to convert to on-campus events in the future. The panel supports SHP’s mission of interdisciplinary innovation, discovery, and education to improve health policy. Our goal is to convene a diverse group of experts from multiple disciplines and career stages to share recent advances and future paths toward health equity.

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Encina Commons,
615 Crothers Way Room 184,
Stanford, CA 94305-6006

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Associate Professor, Health Policy
Senior Fellow, Stanford Institute for Economic Policy Research
Associate Professor, Economics (by courtesy)
rossin-slater_ar21_12_f-cr_compressed.jpg PhD

Maya Rossin-Slater is an Associate Professor of Health Policy at Stanford University School of Medicine. She is also a Senior Fellow at the Stanford Institute for Economic and Policy Research (SIEPR), a Research Associate at the National Bureau of Economic Research (NBER) and a Research Fellow at the Institute of Labor Economics (IZA). She received her PhD in Economics from Columbia University in 2013, and was an Assistant Professor of Economics at the University of California, Santa Barbara from 2013 to 2017, prior to coming to Stanford. Rossin-Slater’s research includes work in health, public, and labor economics. She focuses on issues in maternal and child well-being, family structure and behavior, and policies targeting disadvantaged populations in the United States and other developed countries.

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Associate Professor of Medicine Stanford Health Policy

Encina Commons,
615 Crothers Way
Stanford, CA 94305-6006

 

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Professor, Health Policy
Professor, Computer Science (by courtesy)
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Sherri Rose, Ph.D. is a Professor of Health Policy and, by courtesy, of Computer Science at Stanford University, where she is Director of the Health Policy Data Science Lab. Her research is centered on developing and integrating innovative statistical machine learning approaches to improve human health and health equity. Within health policy, Dr. Rose works on ethical algorithms in health care, risk adjustment, chronic kidney disease, and health program evaluation. She has published interdisciplinary projects across varied outlets, including Biometrics, Journal of the American Statistical Association, Journal of Health Economics, Health Affairs, and New England Journal of Medicine. In 2011, Dr. Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018.

Dr. Rose has been honored with an NIH Director’s Pioneer Award, NIH Director's New Innovator Award, the ISPOR Bernie J. O'Brien New Investigator Award, and multiple mid-career awards, including the Gertrude M. Cox Award. She is a Fellow of the American Statistical Association (ASA) and received the Mortimer Spiegelman Award, which recognizes the statistician under age 40 who has made the most significant contributions to public health statistics. In 2024, she received both the ASHEcon Willard G. Manning Memorial Award for Best Research in Health Econometrics and the ASA Outstanding Statistical Application Award. She was recently awarded the Open Science Champion Prize by Stanford University. Her research has been featured in The New York Times, USA Today, and The Boston Globe. She was Co-Editor-in-Chief of the journal Biostatistics from 2019-2023.

She received her Ph.D. in Biostatistics from the University of California, Berkeley and a B.S. in Statistics from The George Washington University before completing an NSF Mathematical Sciences Postdoctoral Research Fellowship at Johns Hopkins University. 

Director, Health Policy Data Science Lab
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Stanford Health Policy

Encina Commons, Room 220
615 Crothers Way
Stanford, CA 94305-6006

(650) 721-2486 (650) 723-1919
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Professor, Health Policy
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Jeremy Goldhaber-Fiebert, PhD, is a Professor of Health Policy, a Core Faculty Member at the Center for Health Policy and the Department of Health Policy, and a Faculty Affiliate of the Stanford Center on Longevity and Stanford Center for International Development. His research focuses on complex policy decisions surrounding the prevention and management of increasingly common, chronic diseases and the life course impact of exposure to their risk factors. In the context of both developing and developed countries including the US, India, China, and South Africa, he has examined chronic conditions including type 2 diabetes and cardiovascular diseases, human papillomavirus and cervical cancer, tuberculosis, and hepatitis C and on risk factors including smoking, physical activity, obesity, malnutrition, and other diseases themselves. He combines simulation modeling methods and cost-effectiveness analyses with econometric approaches and behavioral economic studies to address these issues. Dr. Goldhaber-Fiebert graduated magna cum laude from Harvard College in 1997, with an A.B. in the History and Literature of America. After working as a software engineer and consultant, he conducted a year-long public health research program in Costa Rica with his wife in 2001. Winner of the Lee B. Lusted Prize for Outstanding Student Research from the Society for Medical Decision Making in 2006 and in 2008, he completed his PhD in Health Policy concentrating in Decision Science at Harvard University in 2008. He was elected as a Trustee of the Society for Medical Decision Making in 2011.

Past and current research topics:

  1. Type 2 diabetes and cardiovascular risk factors: Randomized and observational studies in Costa Rica examining the impact of community-based lifestyle interventions and the relationship of gender, risk factors, and care utilization.
  2. Cervical cancer: Model-based cost-effectiveness analyses and costing methods studies that examine policy issues relating to cervical cancer screening and human papillomavirus vaccination in countries including the United States, Brazil, India, Kenya, Peru, South Africa, Tanzania, and Thailand.
  3. Measles, haemophilus influenzae type b, and other childhood infectious diseases: Longitudinal regression analyses of country-level data from middle and upper income countries that examine the link between vaccination, sustained reductions in mortality, and evidence of herd immunity.
  4. Patient adherence: Studies in both developing and developed countries of the costs and effectiveness of measures to increase successful adherence. Adherence to cervical cancer screening as well as to disease management programs targeting depression and obesity is examined from both a decision-analytic and a behavioral economics perspective.
  5. Simulation modeling methods: Research examining model calibration and validation, the appropriate representation of uncertainty in projected outcomes, the use of models to examine plausible counterfactuals at the biological and epidemiological level, and the reflection of population and spatial heterogeneity.
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Stanford Health Policy
Petra Persson Stanford Department of Economics
Kirsten Bibbins-Domingo UCSF
Samantha Artiga Kaiser Family Foundation
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Digital contact tracing has the potential to limit the spread of COVID-19. A contact-tracing smartphone app that has been readily adopted by people in England and Wales has shown efficacy in reducing disease spread.
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Nature
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C. Jason Wang
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Douglas K. Owens
Jeremy Goldhaber-Fiebert
Joshua Salomon
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Stanford health law experts Michelle Mello and David Studdert discuss the ongoing pandemic, proof of vaccination “passports” at the state and federal levels, and a July 19 ruling that Indiana University could require that its students be vaccinated.
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Michelle Mello
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This interview by Bruce Goldman was originally published by the Stanford School of Medicine.


On May 13, the journal Science published a letter, signed by 18 scientists, stating that it was still unclear whether the virus that causes COVID-19 emerged naturally or was the result of a laboratory accident, but that neither cause could be ruled out. David Relman, MD, the Thomas C. and Joan M. Merigan Professor and professor of microbiology and immunology, spearheaded the effort.

Relman is no stranger to complicated microbial threat scenarios and illness of unclear origin. He has advised the U.S. government on emerging infectious diseases and potential biological threats. He served as vice chair of a National Academy of Sciences committee reviewing the FBI investigation of letters containing anthrax that were sent in 2001. Recently, he chaired another academy committee that assessed a cluster of poorly explained illnesses in U.S. embassy employees. He is a past president of the Infectious Diseases Society of America.

Stanford Medicine science writer Bruce Goldman asked Relman to explain what remains unknown about the coronavirus’s emergence, what we may learn and what’s at stake.

1. How might SARS-CoV-2, which causes COVID-19, have first infected humans?

Relman: We know very little about its origins. The virus’s closest known relatives were discovered in bats in Yunnan Province, China, yet the first known cases of COVID-19 were detected in Wuhan, about 1,000 miles away.

There are two general scenarios by which this virus could have made the jump to humans. First, the jump, or “spillover,” might have happened directly from an animal to a human, by means of an encounter that took place within, say, a bat-inhabited cave or mine, or closer to human dwellings — say, at an animal market. Or it could have happened indirectly, through a human encounter with some other animal to which the primary host, presumably a bat, had transmitted the virus.

Bats and other potential SARS-CoV-2 hosts are known to be shipped across China, including to Wuhan. But if there were any infected animals near or in Wuhan, they haven’t been publicly identified.

Maybe someone became infected after contact with an infected animal in or near Yunnan, and moved on to Wuhan. But then, because of the high transmissibility of this virus, you’d have expected to see other infected people at or near the site of this initial encounter, whether through similar animal exposure or because of transmission from this person.

2. What’s the other scenario?

Relman: SARS-CoV-2 could have spent some time in a laboratory before encountering humans. We know that some of the largest collections of bat coronaviruses in the world — and a vigorous research program involving the creation of “chimeric” bat coronaviruses by integrating unfamiliar coronavirus genomic sequences into other, known coronaviruses — are located in downtown Wuhan. And we know that laboratory accidents happen everywhere there are laboratories.

Humans are fallible, and laboratory accidents happen — far more often than we care to admit.
David Relman
Senior Fellow, CISAC

All scientists need to acknowledge a simple fact: Humans are fallible, and laboratory accidents happen — far more often than we care to admit. Several years ago, an investigative reporter uncovered evidence of hundreds of lab accidents across the United States involving dangerous, disease-causing microbes in academic institutions and government centers of excellence alike — including the Centers for Disease Control and Prevention and the National Institutes of Health.

SARS-CoV-2 might have been lurking in a sample collected from a bat or other infected animal, brought to a laboratory, perhaps stored in a freezer, then propagated in the laboratory as part of an effort to resurrect and study bat-associated viruses. The materials might have been discarded as a failed experiment. Or SARS-CoV-2 could have been created through commonly used laboratory techniques to study novel viruses, starting with closely related coronaviruses that have not yet been revealed to the public. Either way, SARS-CoV-2 could have easily infected an unsuspecting lab worker and then caused a mild or asymptomatic infection that was carried out of the laboratory.

3. Why is it important to understand SARS-CoV-2’s origins?

Relman: Some argue that we would be best served by focusing on countering the dire impacts of the pandemic and not diverting resources to ascertaining its origins. I agree that addressing the pandemic’s calamitous effects deserves high priority. But it’s possible and important for us to pursue both. Greater clarity about the origins will help guide efforts to prevent a next pandemic. Such prevention efforts would look very different depending on which of these scenarios proves to be the most likely.

Evidence favoring a natural spillover should prompt a wide variety of measures to minimize human contact with high-risk animal hosts. Evidence favoring a laboratory spillover should prompt intensified review and oversight of high-risk laboratory work and should strengthen efforts to improve laboratory safety. Both kinds of risk-mitigation efforts will be resource intensive, so it’s worth knowing which scenario is most likely.

4. What attempts at investigating SARS-CoV-2’s origin have been made so far, with what outcomes?

Relman: There’s a glaring paucity of data. The SARS-CoV-2 genome sequence, and those of a handful of not-so-closely-related bat coronaviruses, have been analyzed ad nauseam. But the near ancestors of SARS-CoV-2 remain missing in action. Absent that knowledge, it’s impossible to discern the origins of this virus from its genome sequence alone. SARS-CoV-2 hasn’t been reliably detected anywhere prior to the first reported cases of disease in humans in Wuhan at the end of 2019. The whole enterprise has been made even more difficult by the Chinese national authorities’ efforts to control and limit the release of public health records and data pertaining to laboratory research on coronaviruses.

In mid-2020, the World Health Organization organized an investigation into the origins of COVID-19, resulting in a fact-finding trip to Wuhan in January 2021. But the terms of reference laying out the purposes and structure of the visit made no mention of a possible laboratory-based scenario. Each investigating team member had to be individually approved by the Chinese government. And much of the data the investigators got to see was selected prior to the visit and aggregated and presented to the team by their hosts.

The recently released final report from the WHO concluded — despite the absence of dispositive evidence for either scenario — that a natural origin was “likely to very likely” and a laboratory accident “extremely unlikely.” The report dedicated only 4 of its 313 pages to the possibility of a laboratory scenario, much of it under a header entitled “conspiracy theories.” Multiple statements by one of the investigators lambasted any discussion of a laboratory origin as the work of dark conspiracy theorists. (Notably, that investigator — the only American selected to be on the team — has a pronounced conflict of interest.)

Given all this, it’s tough to give this WHO report much credibility. Its lack of objectivity and its failure to follow basic principles of scientific investigation are troubling. Fortunately, WHO’s director-general recognizes some of the shortcomings of the WHO effort and has called for a more robust investigation, as have the governments of the United States, 13 other countries and the European Union.

5. What’s key to an effective investigation of the virus’s origins?

Relman: A credible investigation should address all plausible scenarios in a deliberate manner, involve a wide variety of expertise and disciplines and follow the evidence. In order to critically evaluate other scientists’ conclusions, we must demand their original primary data and the exact methods they used — regardless of how we feel about the topic or about those whose conclusions we seek to assess. Prior assumptions or beliefs, in the absence of supporting evidence, must be set aside.

Investigators should not have any significant conflicts of interest in the outcome of the investigation, such as standing to gain or lose anything of value should the evidence point to any particular scenario.

There are myriad possible sources of valuable data and information, some of them still preserved and protected, that could make greater clarity about the origins feasible. For all of these forms of data and information, one needs proof of place and time of origin, and proof of provenance.

To understand the place and time of the first human cases, we need original records from clinical care facilities and public health institutions as well as archived clinical laboratory data and leftover clinical samples on which new analyses can be performed. One might expect to find samples of wildlife, records of animal die-offs and supply-chain documents.

Efforts to explore possible laboratory origins will require that all laboratories known to be working on coronaviruses, or collecting relevant animal or clinical samples, provide original records of experimental work, internal communications, all forms of data — especially all genetic-sequence data — and all viruses, both natural and recombinant. One might expect to find archived sequence databases and laboratory records.

Needless to say, the politicized nature of the origins issue will make a proper investigation very difficult to pull off. But this doesn’t mean that we shouldn’t try our best. Scientists are inquisitive, capable, clever, determined when motivated, and inclined to share their insights and findings. This should not be a finger-pointing exercise, nor an indictment of one country or an abdication of the important mission to discover biological threats in nature before they cause harm. Scientists are also committed to the pursuit of truth and knowledge. If we have the will, we can and will learn much more about where and how this pandemic arose.  

relman

David Relman

Senior Fellow at the Freeman Spogli Institute for International Studies
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Microbiologist David Relman discusses the importance of understanding how the coronavirus emerged.

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Peter ("Pete") W. Groeneveld, MD, MS is Professor of Medicine at the University of Pennsylvania’s Perelman School of Medicine and a primary care physician at Philadelphia’s Corporal Michael J. Crescenz VA Medical Center. He is the Founding Director of Penn’s Cardiovascular Outcomes, Quality, and Evaluative Research (CAVOQER) Center, Director of Research at Penn’s Leonard Davis Institute of Health Economics (LDI), Chair of the VA’s Research and Development Committee, Co-Director of Penn’s Master of Science in Health Policy (MSHP) program, and Associate Director of the VA’s Center for Health Equity Research and Promotion. Dr. Groeneveld’s research is focused on the quality, outcomes, costs, and equity of high-technology cardiovascular care, and his methodological expertise is in the analysis of a wide variety of health care data, including administrative claims, clinical registries, electronic medical records, and surveys. His research has been funded by the VA, NIH, AHRQ, and the Commonwealth of Pennsylvania, and he has co-authored over 100 peer-reviewed publications. Dr. Groeneveld is a Fellow of the American Heart Association and of the American College of Physicians, and he is an elected member of the American Society for Clinical Investigation (ASCI).

Title: Cardiology Physician Group Practice Vertical Integration and the Use of Cardiovascular Imaging

Abstract: A substantial proportion of previously independent U.S. cardiology physician practices have become vertically integrated into larger health systems.  It is unclear if vertical integration affected the clinical practice patterns of these cardiologists.  Longitudinal data from cardiology practice surveys from 2008-2013 were combined with Medicare fee-for-service claims for two common cardiology imaging tests: echocardiograms and cardiac nuclear studies. Cardiologists who transitioned from independent to hospital- or health system-owned practices ordered 17% more echocardiograms and 10% more cardiac nuclear imaging studies after their practices had transitioned.  Our findings surprisingly suggest that vertical integration of cardiologists' practices was associated with higher rates of cardiovascular imaging.  Potential explanations include preferential integration of group practices with lower pre-integration imaging rates, increased post-integration clinician incentives for ordering tests, and/or reduced administrative barriers to obtaining testing after integration. 

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Peter W. Groeneveld, MD, MS
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