Public 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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Stanford Health Policy Health Equity Panel Card

Virtual Zoom 

Register in advance for this meeting using this link:
https://stanford.zoom.us/meeting/register/tJwrfuutqjotG92GrEvzvD29LNgpME3ympvx 

After registering, you will receive a confirmation email containing a Zoom link and details about joining the meeting.

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)
PhD Program Director
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)
sherri_rose-portrait.jpg PhD

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
jeremy-fisch_profile_compressed.jpg PhD

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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Diabetes is one of the fastest-growing health challenges of the 21st century. On the frontlines of the epidemic rise in the number of people with diabetes is the Asia-Pacific region. China, in particular, has by far the largest absolute burden of diabetes, with an estimated 116 million adults living with the disease accounting for one-quarter of patients with diabetes globally. By 2045, the number of adults living with diabetes in the country is expected to increase to 147 million, not including the large diaspora community China provides worldwide.

Evaluating the health and economic outcomes of diabetes and its complications is vital for formulating health policy. The existing predictive outcomes models for type 2 diabetes, however, were developed and validated in historical European populations and may not be applicable for East Asian populations with their distinct epidemiology and complications. Additionally, the existing models are typically limited to diabetes alone and ignore the progression from prediabetes to diabetes. The lack of an appropriate simulation model for East Asian individuals and prediabetes is a major gap for the economic evaluation of health interventions.

New collaborative research now addresses these limitations. The research team includes APARC’s Asia Health Policy Program Director Karen Eggleston. The researchers developed and validated a patient-level simulation model for predicting lifetime health outcomes of prediabetes and type 2 diabetes in East Asian populations. They report on their findings in the journal PLOS Medicine


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Modeling Health Outcomes Among East Asian Populations

The chronic progression to diabetes-related complications is apt for computer simulation modeling due to the long-term nature of health outcomes and the time lag for interventions to impact patient outcomes. It is problematic, however, to estimate the impacts of health interventions on East Asian populations with diabetes using existing models, which were developed and validated in European and North American populations with different epidemiology and outcomes.

To fill in this gap, Eggleston and her colleagues set out to develop and validate an outcomes model for the progression of diabetes and related complications in Chinese populations. They compared this new model, called the Chinese Hong Kong Integrated Modeling and Evaluation (CHIME), to two widely used existing models developed and validated in the United Kingdom (known as the United Kingdom Prospective Diabetes Study Outcomes Model 2, or UKPDS-OM2) and in the United States/Canada (called Risk Equations for Complications of type 2 Diabetes, or RECODe). Despite the continuum of risk across the spectrum of risk factor values, these two existing models ignore the progression from prediabetes to diabetes.

The CHIME integrates prediabetes and diabetes into a comprehensive model comprising 13 outcomes. These include mortality, micro- and macrovascular complications, and the development of diabetes. The researchers developed the CHIME simulation model using data from a population-based cohort of 97,628 participants in Hong Kong with type 2 diabetes (43.5%) or prediabetes (56.5%) from 2006 to 2017. Known as the Hong Kong Clinical Management System (CMS), this cohort makes one of the largest Chinese electronic health informatics systems with detailed clinical records. 

The CHIME outperformed the widely used United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) and Risk Equations for Complications of type 2 Diabetes (RECODe) models on real-world data.
Karen Eggleston et al

The next step was to externally validate the CHIME model against individual-level data from the China Health and Retirement Longitudinal Study (CHARLS) cohort (2011-2018), a nationally representative longitudinal cohort of middle-aged and elderly Chinese residents age 45 and older. The researchers validated the CHIME model against six outcomes measures recorded in the CHARLS data and an additional 80 endpoints from nine published trials of diabetes patients using simulated cohorts of 100,000 individuals.

Towards Reducing the Disease Burden of Diabetes

The researchers found that the CHIME model outperformed the widely used UKPDS-OM2 and RECODe models on the data used, meaning that the validation of the CHIME model was more accurate for trials with mainly Asian participants than trials with mostly non-Asian participants. The results indicate that the CHIME model is a validated tool for predicting the progression of diabetes and its outcomes, particularly among Chinese and East Asian populations, for which the existing models have been unsuitable.

With the new model, clinicians and health economists can evaluate population health status for prediabetes and diabetes using routinely recorded data and therapies related to the long-term management of diabetes. In particular, the CHIME outcomes model enables them to assess patients' quality of life and measure cost per quality-adjusted life-years over the long-time horizon of chronic disease conditions. The new model thus supports the economic evaluation of policy guidelines and clinical treatment pathways to tackle diabetes and prediabetes, address micro- and macrovascular complications associated with these conditions, and improve life expectancy.

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