Health policy
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Few issues in the policy response to the coronavirus disease 2019 (COVID-19) pandemic have inspired as impassioned debate as school reopening. There is broad agreement that school closures involve heavy burdens on students, parents, and the economy, with profound equity implications, but also that the risk of outbreaks cannot be eliminated even in a partial reopening scenario with in-school precautions. Consensus largely ends there, however: the approaches states and localities have taken to integrating these concerns into school reopening plans are highly variable.

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Journal Articles
Publication Date
Journal Publisher
JAMA Network
Authors
Jeremy Goldhaber-Fiebert
David Studdert
Michelle Mello
Number
2020
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On August 17, 2020, the Los Angeles Unified School District launched a program to test more than 700,000 students and staff for SARS-CoV-2. The district is paying a private contractor to provide next-day, early-morning results for as many as 40,000 tests daily. As of October 4, a total of 34,833 people had been tested at 42 sites. The program is notable not only because it’s ambitious, but also because it’s unusual: testing is conspicuously absent from school reopening plans in many other districts. Typically, exhaustive attention has instead focused on physical distancing, face coverings, hygiene, staggering of schedules, and cohorting (dividing students into small, fixed groups). Although the Centers for Disease Control and Prevention (CDC), the American Academy of Pediatrics, the National Academies of Sciences, Engineering, and Medicine, and state officials have urged schools to prepare for Covid-19 cases, they have offered strikingly little substantive guidance on testing. Immediate attention to improving testing access and response planning is essential to the successful reopening of schools.

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Journal Articles
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Journal Publisher
New England Journal of Medicine
Authors
Michelle Mello
Number
2020
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Abstract

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrates that these new fair regression methods may lead to massive improvements in group fairness (eg, 98%) with only small reductions in overall fit (eg, 4%).

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Journal Articles
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Journal of the International Biometric Society
Authors
Sherri Rose
Number
2020
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Title: Women Left Behind: Gender Inequality Within Rajasthan's Health Insurance Program

Radhika Jain 
Asia Health Policy Postdoctoral Research Fellow, Shorenstein APARC
Working with Karen Eggleston, PhD, Director of the Asia Health Policy Program, Shorenstein Asia-Pacific Research Center and Fellow at the Center for Health Policy and the Center for Primary Care and Outcomes Research.

Abstract: Using data on millions of hospital visits, we document striking gender disparities under a government health insurance program that entitles 46 million poor households in Rajasthan, India to free hospital care. Young girls and elderly women comprise only 40% of all transactions in their age groups and these gaps are larger for private and tertiary care. The gender gap does not decrease over four years of implementation, despite substantial increases in total utilization. We find evidence consistent with the theory that the gap is driven by households’ willingness to allocate more resources to male than female health. Reducing the cost of care increases levels of utilization as well as male-female disparities. Female political representation reduces disparities, but not among the elderly.     

 

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Radhika Jain
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Title: Is Preference for Gender Concordance Good in Patient-Provider Relationships?

Rebecca Staiger
Postdoctoral Scholar 
Stanford University 
Center for Health Policy and Center for Primary Care & Outcomes Research 

Abstract: Choosing a primary care physician (PCP) of the same gender is a common heuristic used by many patients. However, there is limited evidence as to whether gender concordance in primary care settings produces better health outcomes. Using a novel and largely under-utilized national Medicaid claims database, the Medicaid Analytic eXtract (MAX) files, and an instrumental variables (IV) approach, I evaluate whether gender concordance in the patient-PCP relationship generates good health outcomes among Medicaid managed care enrollees, as measured by improved primary use and the avoidance of hospitalizations and emergency department use. My instrument is based on the availability of male physicians treating other patients in the HSA a particular patient lives in. Preliminary results indicate that while a naive approach (OLS) suggests that gender concordance may lead to better outcomes, adjusting for the endogeneity of patient selection through use of an IV suggests that male PCPs may help both male and female patients achieve better health outcomes.

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Although health care billing claims data have been widely used to study health care use, spending, and policy changes, their use in the study of infectious disease has been limited. Other data sources, including from the Centers for Disease Control and Prevention (CDC), have provided timelier reporting to outbreak experts. However, given the scope of SARS-CoV-2—the causative agent responsible for the novel coronavirus disease 2019 (COVID-19) pandemic—and the multidimensional impact of the crisis on the health care system, analyses relying on health care claims data have begun to appear. Claims-based COVID-19 studies have a role, but it is critical to understand the limitations of these data. We are concerned that many weaknesses are not recognized by those familiar with other forms of patient-level data. Below, we examine several major considerations and make suggestions about where claims data may be best leveraged to inform policy and decision making.

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Journal Articles
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Health Affairs
Authors
Sherri Rose
Number
2020
1
Master's Student, Health Policy
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Tom Handley, from London, England, graduated from Oxford University with a bachelor's degree in medical science, and earned an MD at Imperial College London. After two years of medical practice, he completed an academic foundation training program in general surgery with research in novel transplant technologies. Tom aspires to find policy solutions for challenges facing transplant systems, to widen access to transplant surgery, and improve the delivery of surgical care worldwide. He was awarded Proxime Accessit for the University of London Gold Medal, the prize given to the best graduating doctor from the universities of London, and recently made a Member of the Royal College of Surgeons. He is also a Stanford Knight-Hennessy Scholar.

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Master's Student Alumni, Health Policy
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Griffin is Rocky Mountain born and raised. He graduated from Brigham Young University with a Bachelor of Arts in Middle East Studies and Arabic and went on to become a fellow with the Center for Arabic Study Abroad at the American University in Cairo. Griffin first began thinking about value in healthcare while volunteering at the Egyptian National Cancer Institute, where he witnessed firsthand the challenges of delivering quality care in a resource-limited setting.

The following year, Griffin became a clinical researcher for the Surgical Services Clinical Program at Intermountain Healthcare. Working with a multidisciplinary team, he helped provide surgeons across the Intermountain system with real-time cost and outcomes data to inform the decision-making process at every stage of surgical care. Griffin attended medical school at The Ohio State University College of Medicine and completed the first two years of postgraduate surgical training at Parkland Memorial Hospital and the University of Texas Southwestern in Dallas. Parkland is the public hospital for Dallas County, and seeing the disparities that exist in this large urban community prompted him to pursue additional training in population health.

As a Stanford-Intermountain Fellow, Griffin is working with leaders in the field of delivery science to continue to develop innovative ways to enhance the value of patient care. He will then return to Dallas to finish his surgical residency and continue a career as a surgeon and leader in healthcare delivery. When he’s not at the hospital, Griffin can be found exercising outdoors, cooking with his wife, or fishing from his kayak.

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Vivian Ho MD

Vivian was born and raised in Dallas, Texas and completed her undergraduate degree in Biology at Stanford. She attended the Columbia University College of Physicians & Surgeons in New York for medical school, and returned to Stanford for her Integrated Vascular Surgery residency. As part of her program, Vivian has completed 3 years of clinical training and is now taking a two year fellowship to pursue her interest in academic surgery.

Vivian’s research leverages epidemiological and machine learning methods to evaluate surgical diagnostics and decision-making tools, with the goal of reducing unnecessary testing and streamlining the pathway from diagnosis to intervention. Previously, she has used national databases to delineate gender differences in outcomes of aortic surgery and the effect of systemic anticoagulation on patients with traumatic aortic injury. She is particularly interested in using the electronic medical record as a source of clinical data and a platform for clinical decision-making support. She will be pursuing a Masters in Biomedical Informatics at Stanford from 2020-2022 to refine her computational techniques.

In her free time Vivian enjoys cooking, biking, and playing tennis.

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PhD Student, Health Policy
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Amanda Su is a Health Policy PhD candidate specializing in health economics. Her research interests include healthcare access, delivery, financing, and utilization.

Before Stanford, Amanda was a data scientist at Nuna Health, where she used econometric, statistical, and machine learning techniques to develop and improve an offline patient-PCP matching system. Prior, at Analysis Group, Amanda helped conduct economic analyses, market power studies, and survey experiments to study firm and consumer behavior. Amanda obtained her bachelor's degrees in Economics and Business Administration from the University of California, Berkeley.

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