Health and Medicine

FSI’s researchers assess health and medicine through the lenses of economics, nutrition and politics. They’re studying and influencing public health policies of local and national governments and the roles that corporations and nongovernmental organizations play in providing health care around the world. Scholars look at how governance affects citizens’ health, how children’s health care access affects the aging process and how to improve children’s health in Guatemala and rural China. They want to know what it will take for people to cook more safely and breathe more easily in developing countries.

FSI professors investigate how lifestyles affect health. What good does gardening do for older Americans? What are the benefits of eating organic food or growing genetically modified rice in China? They study cost-effectiveness by examining programs like those aimed at preventing the spread of tuberculosis in Russian prisons. Policies that impact obesity and undernutrition are examined; as are the public health implications of limiting salt in processed foods and the role of smoking among men who work in Chinese factories. FSI health research looks at sweeping domestic policies like the Affordable Care Act and the role of foreign aid in affecting the price of HIV drugs in Africa.

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"Trade-offs of simplifying complex choices: early evidence from the ACA Exchages"

 

Please note: All research in progress seminars are off-the-record. Any information about methodology and/or results is embargoed until publication.

 

Using new data from the early years of the federally-facilitated Health Insurance Marketplaces (or ACA Exchanges), we explore which factors affect the health insurance choices of the non-elderly population targeted by the ACA. A growing literature has documented potential behavioral biases and high cost of decision-making in various insurance settings that rely on consumer choice - from retirement savings to prescription drug plans. A natural conclusion from this literature is that it may be optimal for policy-makers to introduce behavioral nudges (e.g. optimal defaults, framing) that could reduce the behavioral biases or decision-making costs. For example, ACA Exchanges use "metal level" classification of plans as a framing that reduces the complexity of comparisons across dozens of plans on the Exchanges. So far, we have little evidence on how such nudges work in practice, and whether they are strictly welfare-improving or may lead to unintended consequences. In this project we attempt to start closing this gap by exploring whether the metal tier framing affects choices in the federally facilitated Health Insurance Marketplaces.

Maria Polyakova Health Research and Policy
Seminars
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"Plastic Surgery or Primary Care? Altruistic Preferences and Expected Specialty Choice of U.S. Medical Students"

 

Please note: All research in progress seminars are off-the-record. Any information about methodology and/or results is embargoed until publication.

 

Understanding physicians' decisions when faced with conflicts between their own financial self-interest and patients? economic or health interests is of key importance in health economics and policy. This issue is especially salient in certain medical specialties where less altruistic behavior of physicians can yield significant financial gains. This study adopts an experimental approach to examine altruistic preferences of medical students from schools around the U.S. and whether these preferences predict those students? expected medical specialty choice. The experimental design consists of a set of computer-based revealed preference decision problems which ask the experimental subjects to allocate real money between themselves and an anonymous person. These data are used to derive an innovative measure of altruism for each participant which we are the first to apply in health economics. We then examine the association between altruism and expected specialty choice, after controlling for an extensive set of covariates collected from a survey questionnaire which we fielded. We find substantial heterogeneity in altruistic preferences among experimental subjects. Medical students with a lower degree of altruism are significantly more likely to choose high-income specialties. This altruism measure is more predictive of specialty than a wide range of other characteristics including parental income, student loan amount and Medical College Admission Test (MCAT) score.

Jing Li PhD Candidate UC Berkeley
Seminars
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"Measuring the Impact of Nurse Staffing on Patient Outcomes: The Effect of Data Aggregation and Estimation Methods"

 

Please note: All research in progress seminars are off-the-record. Any information about methodology and/or results is embargoed until publication.

 

Research Objectives: A growing body of evidence shows that nurse staffing levels and composition affect patient outcomes.  This evidence has come from difference data sources, with different levels of data aggregation, and used different estimation methods.  The problem of unobserved heterogeneity (unobserved characteristics that affect outcomes) is large for this area of research and estimates that don’t address this are almost certainly biased.  We used a large, longitudinal, patient-level dataset with monthly, unit-level nurse staffing data to examine how different levels of data aggregation and different statistical methods affected the estimates of the effect of nurse staffing on patient outcomes.

Study Design:  Monthly staffing for each unit, for each type of nurse (registered nurse, Licensed Practical Nurses, nursing aides, contract nurses), were obtained from VA accounting data.  Payroll data provided education levels and how ng each nurse had worked on the unit (unit tenure).  Patient characteristics and length of stay (LOS) were obtained from VA hospital discharge records.  Log(LOS) was used as the dependent variable as it captures the effect of many nursing-sensitive patient outcomes.  The model controlled for patient age, expected LOS, and patient co-morbidities; the variables of interest were nurse staffing, nurse skill-mix, and unit tenure.  The models were estimated using both ordinary least squares (OLS) and fixed-effects (FE) regressions; the latter was used to address unobserved heterogeneity.  All regressions were patient-level, with different levels of aggregation for the nurse staffing variables (unit-month, unit-year, hospital-month, and hospital-year) and the unit level models were estimated for all units together, and separately for acute care units and intensive care units. 

Population Studied:  All VA acute medical care units (including ICUs) for 2003-2006.  1,923,048 patients from 427 units across 138 VA Medical Centers.

Principal Findings:  The results were quite sensitive to both estimation method and unit of aggregation.  The change in the point estimates of the effects of nurse staffing on LOS of switching from monthly to annual staffing data ranged from 14-1177% for the FE models and 13-276% (plus two reversals, -0.20 to 0.27 and -0.09 to 0.40) for the OLS models.  These ranges were even larger across all levels of aggregation.  For the same level of aggregation, the difference between the OLS and FE estimates ranged from 0-304% and there were two cases of sign reversal (-0.21 to 0.27 and -0.19 to 0.30).

Conclusions:  The magnitude and even the direction of the effects of different elements of nurse staffing on patient outcomes are quite sensitive to the level of aggregation and estimation method. 

Implications for Policy or Practice:  Interpretation of the results of studies of nurse staffing on patient outcomes needs to account for the level of data aggregation and the statistical methods used.  Higher levels of aggregation, both across time and across units, probably masks effects.  Thus, studies that measure nurse staffing at the unit-level data with shorter time intervals yield more reliable estimates.  Studies that fail to account for unobserved heterogeneity are probably biased.  But, FE models also have limits, as they only estimate marginal effects and can’t directly compare the effects of high vs. low staffing levels.

Ciaran S. Phibbs
Seminars
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"Equalizing child sex ratios in India: Understanding the trends, distribution, composition, potential drivers, and impact on fertility"

 

Please note: All research in progress seminars are off-the-record. Any information about methodology and/or results is embargoed until publication.

 

Abstract

I will present on the findings of my research on the changing patterns of child sex ratios in India, this includes an exploration of whether child sex ratios are improving in districts with the most uneven child sex ratios in recent years, and what factors are associated with this improvement. I also decompose the improvements in child sex ratios into improvements due to less sex selective abortion vs improved girl child mortality compared to boy child mortality. I then discuss initial findings on potential drivers of the improvement, with the hope to spark a discussion on other ways to think about these findings, specifically related to measuring the impact of policies. Finally, I will present some very preliminary findings from a work in progress on how sex preferences are impacting overall fertility in India.

Nadia Diamond-Smith Postdoctoral Fellow UCSF
Seminars
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"Re-tooling cost-effectiveness analysis for global health relevance"

Please note: All research in progress seminars are off-the-record. Any information about methodology and/or results is embargoed until publication.

To identify priorities for action in global health, decision makers need information on the potential impact, costs and cost effectiveness of different possible choices regarding health technologies and interventions. A large volume of cost-effectiveness analysis has been produced to try to meet this need, but its impact on policies and programs in low- and middle-income countries has evidently been limited. In this seminar we will explore some possible reasons for the relatively modest policy impact of cost-effectiveness analysis in global health and propose directions for re-thinking the approaches and methods that are commonly used in the field. Drawing examples from our recent and ongoing research in areas such as HIV/AIDS, tuberculosis and maternal and child health, we will describe an agenda to pivot the practice of decision science in global health toward a more systematic approach to comparative strategy evaluation.

 

Joshua Salomon Professor of Global Health Harvard School of Public Health
Seminars
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"Wisdom of the Crowd or Tyranny of the Mob? OrderRex: Data-Mining Electronic Health Records for Clinical Decision Support"

 

Please note: All research in progress seminars are off-the-record. Any information about methodology and/or results is embargoed until publication.

 

Background

Uncertainty and undesirable variability is pervasive in medical decision making.  Clinical decision support like order sets help distribute expertise, but are constrained by resource intensive manual development. 

Objective

To overcome scalability limitations by automatically generating decision support content from existing practice patterns, analogous to Amazon.com’s product recommender.  To perform the first structured validation of such a system against external standards-of-care and outcome predictions.

Methods

We extracted deidentified electronic health record data from all hospitalizations at Stanford Hospital in 2011 (>5.4M structured data items from >19K patients) to build a system with association statistics for 811 clinical orders (e.g., labs, imaging, medications) and clinical outcomes.  We manually reviewed the National Guideline Clearinghouse for diagnoses of chest pain, gastrointestinal hemorrhage, and pneumonia.  We compared system generated clinical orders against guideline referenced orders by receiver operating characteristic (ROC) analysis.  Human authored order sets provided real-world benchmarks.  We compared predicted vs. actual outcomes by ROC analysis for separate validation patients.

Results

System generated orders were overall consistent with guidelines (ROC AUC c-statistics 0.89, 0.95, 0.83) and improve upon statistical prevalence (0.76, 0.74, 0.73) and pre-existing order sets (0.81, 0.77, 0.73) (P<10-30 in all cases).  Clinical outcome prediction ROC AUC c-statistics were 0.84 for 30 day mortality , 0.84 for 1 week ICU life support, 0.80 for 1 week discharge / length of stay, and 0.68 for 30 day readmission.

Conclusions

Automatically generated order suggestions can reproduce and even optimize manual constructs like order sets while remaining largely concordant with guidelines and avoiding inappropriate recommendations.  This has even more important implications for prevalent cases where well-defined guidelines and order sets do not exist.  The same methodology is predictive of clinical outcomes comparable to state-of-the-art prognosis models (e.g., APACHE II), pointing to opportunities to link suggestions against favorable outcomes.

Jonathan Chen
Seminars
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The H5N1 strain of the bird flu is a deadly virus that kills more than half of the people who catch it.

Fortunately, it’s not easily spread from person to person, and is usually contracted though close contact with infected birds.

But scientists in the Netherlands have genetically engineered a much more contagious airborne version of the virus that quickly spread among the ferrets they use as an experimental model for how the disease might be transmitted among humans.

And researchers from the University of Wisconsin-Madison used samples from the corpses of birds frozen in the Arctic to recreate a version of the virus similar to the one that killed an estimated 40 million people in the 1918 flu pandemic.

It’s experiments like these that make David Relman, a Stanford microbiologist and co-director of the Center for International Security and Cooperation, say it's time to create a better system for oversight of risky research before a man-made super virus escapes from the lab and causes the next global pandemic.

“The stakes are the health and welfare of much of the earth’s ecosystem,” said Relman.

“We need greater awareness of risk and a greater number of different kinds of tools for regulating the few experiments that are going to pose major risks to large populations of humans and animals and plants.”

Terrorists, rogue states or conventional military powers could also use the published results of experiments like these to create a deadly bioweapon.

“This is an issue of biosecurity, not just biosafety,” he said.

“It’s not simply the production of a new infectious agent, it’s the production of a blueprint for a new infectious agent that’s just as risky as the agent itself.”

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H5N1 bird flu seen under an electron microscope. The virus is colored gold. Photo credit: CDC
Scientists who conduct this kind of research argue that their labs, which follow a set of safety procedures known at Biosafety Level 3, are highly secure and the chances of a genetically engineered virus being released into the general population are almost zero.

But Relman cited a series of recent lapses at laboratories in the United States as evidence that accidents can and do happen.

“There have been a frightening number of accidents at the best laboratories in the United States with mishandling and escape of dangerous pathogens,” Relman said.

“There is no laboratory, there is no investigator, there is no system that is foolproof, and our best laboratories are not as safe as one would have thought.”

The Centers for Disease Control and Prevention (CDC) admitted last year that it had mishandled samples of Ebola during the recent outbreak, potentially exposing lab workers to the deadly disease.

In the same year, a CDC lab accidentally contaminated a mild strain of the bird flu virus with deadly H5N1 and mailed it to unsuspecting researchers.

And a 60 year-old vial of smallpox (the contagious virus that was effectively eradicated by a worldwide vaccination program) was discovered sitting in an unused storage room at a U.S. Food and Drug Administration lab.

Earlier this year, the U.S. Army accidentally shipped samples of live anthrax to hundreds of labs around the world.

Similar problems have been reported in labs around the world. The United Kingdom has had more than 100 mishaps in its high-containment labs in recent years.

It’s difficult to judge the full scope of the problem, because many lab accidents are underreported.

Studying viruses in the lab does bring important potential benefits, such as the promise of universal vaccines, as well as cheap and effective ways of developing new drugs and other kinds of alternative defenses against naturally occurring diseases.

“It’s a very tricky balancing act,” Relman said.

“We don’t want to simply shut down the work or impede it unnecessarily.”

However, there are safer ways to conduct research, such as using harmless “avirulent” versions of the virus that would not cause widespread death and injury if it infected the general public, Relman said.

Developing better tools for risk-benefit analysis to identify and mitigate potential dangers in the early stages of research would be another important step towards making biological experiments safer.

Closer cooperation among diverse stakeholders (including domain experts, government agencies, funding groups, governing organizations of scientists and the general public) is also needed in order to develop effective rules for oversight and regulation of dangerous experiments, both domestically and abroad.

“We believe that the solutions are going to have to involve a diverse group of actors that has not yet been brought together,” Relman said.

“We need new approaches for governance in the life sciences that allow for these kinds of considerations across the science community and the policy community.”

You can read more about Relman’s views on how to limit the risks of biological engineering in this article he wrote for Foreign Affairs with co-author with Marc Lipsitch, director of Harvard’s Center for Communicable Disease Dynamics.

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Co-sponsored by the Health Economics Seminar

We present the first direct evidence on the relative quality of public and private healthcare in a low-income setting, using a unique set of audit studies. We sent standardized (fake) patients to rural primary care providers in the Indian state of Madhya Pradesh, and recorded the quality of care provided and prices charged in each interaction. We report three main findings. First, most private providers lacked formal medical training, but they spent more time with patients and completed more essential checklist items than public providers and were equally likely to provide a correct treatment. Second, we compare the performance of qualified public doctors across their public and private practices and find that the same doctors exerted higher effort and were more likely to provide a correct treatment in their private practices. Third, in the private sector, we find that prices charged are positively correlated with provider effort and correct treatment, but also with unnecessary treatments. In the public sector, we find no correlation between provider salaries and any measure of quality. We develop a simple theoretical framework to interpret our results and show that in settings with low levels of effort in the public sector, the benefits of higher diagnostic effort in the private sector may outweigh the costs of market incentives to over treat. These differences in provider effort may partly explain the dominant market share of fee-charging private providers even in the presence of a system of free public healthcare.

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Karthik Muralidharan is an associate professor of economics at the University of California, San Diego where he joined the faculty as an assistant professor in 2008.

Born and raised in India, he earned an A.B. in economics (summa cum laude) from Harvard, an M.Phil. in economics from Cambridge (UK), and a Ph.D. in economics from Harvard. He is a Research Associate of the National Bureau of Economic Research (NBER), an Affiliate at the Bureau for Research and Economic Analysis of Development (BREAD), a Member of the Jameel Poverty Action Lab (J-PAL) network, an Affiliate at the Center for Effective Global Action (CEGA), and a Research Affiliate with Innovations for Poverty Action (IPA).

Prof. Muralidharan's primary research interests include development, public, and labor economics. Specific topics of interest include education, health, and social protection; measuring quality of public service delivery; program evaluation; and improving the effectiveness of public spending (with a focus on developing countries). Courses taught include undergraduate and graduate classes in development economics, program evaluation, and the economics of education.

 

Quality and Accountability in Healthcare Delivery: Audit-Study Evidence from Primary Care in India
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Quality and Accountability in Healthcare Delivery: Audit-Study Evidence from Primary Care in India
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Karthik Muralidharan Associate Professor of Economics at the University of California, San Diego
Seminars
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Publicly provided long-term care (LTC) insurance with means-tested benefits is suspected to crowd out either private LTC insurance (Brown and Finkelstein, 2008), private saving (Gruber and Yelowitz, 1999; Sloan and Norton, 1997), or informal care (Pauly, 1990; Zweifel and Strüwe, 1997). This contribution predicts crowding-out effects for both private LTC insurance and informal care on the one hand and private saving and informal care on the other. These effects result from the interaction of a parent who decides about private LTC insurance before retirement and the amount of saving in retirement and a caregiver who decides about effort devoted to informal care. Some of the predictions are tested using a recent survey from China.

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Peter Zweifel is an Emeritus of the University of Zurich. After a postdoc position with  the University of Wisconsin-Madison in 1974-75, he received tenure with the University of Zurich in 1984. Publications include more than 100 articles in refereed journals (AER, EnJ, EurJHE, JHE, JRI, JRU, PubCh) as well as Health Economics (with F. Breyer und M. Kifmann) and Insurance Economics (with R. Eisen); Energy Economics (with G. Erdmann and A. Praktiknjo) will be available by the end of 2015.

Long-term care: Is there crowding out of informal care, private insurance as well as saving?
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Peter Zweifel Emeritus, University of Zurich, Switzerland
Seminars
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Abstract: The threat of biological attack on the people of the United States and the world, whether intentional, natural or accidental, is of growing concern, both in spite of and because of significant technological advances over the past four decades. As a global leader, the United States needs a comprehensive policy approach for managing future attacks, which incorporates technologic elements from rapid detection through appropriate response. American and international responses to recent infectious disease outbreaks such as anthrax (intentional, accidental), H5N1 influenza (natural) and ebola (natural) have managed to contain these events ‐ with the paradoxical effect on policy makers, both political and administrative, of relief (“missed that bullet”, “we must be doing this right”), rather than serving as wake‐up calls. A challenge in merging technological solutions into policy lies in the rapid advances across the multiple sciences. Translation of these ongoing technologic advances for policy leaders is an essential element in effective policy development. Incorporation of technologic solutions into biosecurity policy construction, combined with motivated leadership, has the potential for enhancing future national and global responses to unprecedented biological attacks.

About the Speaker: Patrick J. Scannon, M.D., Ph.D. is XOMA's Company Founder, Executive Vice President, Chief Scientific Officer and a member of its Board of Directors. Since 1980, Dr. Scannon has directed the Company's product identification, evaluation and clinical testing programs for novel therapeutic monoclonal antibodies and proteins against infectious, oncologic, metabolic and immunologic diseases. As Chief Scientific Officer, he leads evaluations for new therapeutic antibody identification and discovery programs. 

Dr. Scannon holds a Ph.D. in organic chemistry from the University of California, Berkeley and an M.D. from the Medical College of Georgia. He completed his medical internship and residency in internal medicine at the Letterman Army Medical Center in San Francisco. A board-certified internist, Dr. Scannon is also a member of the American College of Physicians. He is the inventor or co-inventor of several issued U.S. patents, and has published numerous scientific abstracts and papers.

Dr. Scannon has served as a member of the Research Committee for Infectious Diseases Society of America (IDSA), the National Biodefense Science Board (NBSB, a federal advisory board for the Department of Health and Human Services), the chair of the Chem/Bio Warfare Defense Panel for the Defense Threat Reduction Agency (DTRA) and a member of the Defense Sciences Research Council (DSRC, a research board for Defense Advanced Research Projects Agency (DARPA)). He has served as a Trustee of the University of California Berkeley Foundation and as a member of the University of California Berkeley Chancellor's Community Advisory Board. Dr. Scannon is currently on the Board of Directors of Pain Therapeutics, Inc.

Technology Impact on Biosecurity Policy and Practice
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Patrick J. Scannon Founder, Executive Vice President, Chief Scientific Officer XOMA
Seminars
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