Transplantation in China: update

I returned Sunday from a busy and potentially productive trip to China.

Since 2015 it has been illegal in China to use organs from executed prisoners for transplants. The passage of that law was the result of a long struggle between an opaque, often black market system of transplantation, and an emerging transparent system based on voluntary donation.  The transparent system has made, and is continuing to make, enormous strides.

In Shenzhen I visited the China Organ and Transplant Response System (COTRS), run by Dr. Haibo Wang, which organizes and records the data of transplant patients and donors. 

It also collects large amounts of data on all hospital stays at China’s largest hospitals. Together with the National Institute of Health Data Science at Peking University, run by Dr. Luxian Zhang, they are assembling a vast data resource that will have many uses.

In Beijing I also visited the China Organ Transplant Development Foundation, run by Dr. Jeifu Huang, which plays a role in guiding the emerging body of legislation through which transplants are being organized in China with increased transparency.

I also spoke at the Beijing Summit on Health Data Science.

It was a busy week that left me optimistic that we'll see continued big progress in healthcare delivery in China, including but not limited to transplantation.

Some photos were taken...

Palgiarism detection, student data, and Ed Tech: the purchase of Turnitin

Here's a story that caught my eye in the Chronicle of Higher Ed, about the purchase of Turnitin, known so far primarily for plagiarism detection software:

Why a Plagiarism-Detection Company Is Now a Billion-Dollar Business

"Stamping out student plagiarism is big business. How big? $1.735 billion, to be exact. That’s the price that Advance, a privately held media, communications, and technology company, will pay to purchase Turnitin, the 800-pound gorilla of plagiarism-detection services.
...
"While its roots are in plagiarism detection, Turnitin actually has a broader portfolio. For example, it owns Gradescope, which offers AI-assisted grading tools, and Lightside Labs, which uses machine learning to provide feedback on students’ writing.

Chris Caren, chief executive of Turnitin, said the company’s next step is to become a platform for colleges and high schools to submit all types of student assignments, digital or on paper. It would then use AI to help instructors review that work to, among other things, spot at-risk students and devise remediation plans. The company is also developing Turnitin’s software to branch out into the STEM fields and detect plagiarism in coding, for example. In other words, it hopes to become a one-stop shop for all sorts of tech-driven teaching services."

Congratulations to Ed Glaeser, Scott Kominers, Mike Luca and Nikhil Naik (EI best paper award)

Congratulations to the authors of this fine paper, published in Economic Inquiry.

2018 Best EI Article Award Announced!

BIG DATA AND BIG CITIES: THE PROMISES AND 

LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE -- Volume 56, Issue 1, January 2018, Pages: 114-137
by Edward L. Glaeser, Scott Duke Kominers, Michael Luca, and Nikhil Naik

Abstract
"New, “big data” sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services."


The paper's publication history says something about publishing, on line versus in print, at least in Economics.

Publication History

• Issue Online:27 November 2017
• Version of Record online:12 July 2016
• Manuscript accepted:23 February 2016
• Manuscript received:23 November 2015

Coffee for personal data

Inside Higher Ed has the story:
Café Swaps Espresso for Personal Info
A Japanese café chain plans to spread among Ivy League and other top campuses, offering free coffee and tea in exchange for students' personal information and consent to be contacted by companies.

"The cashless Shiru cafés give out handmade coffee and tea drinks for free. In exchange, students flash a university ID and, in the bargain, hand over a small cache of personal information: name, age, email address, interests, major and graduation year, among other details. They also agree to be contacted by Shiru’s corporate sponsors, who underwrite all those cappuccinos, matcha lattes and iced Americanos.
...
"Starbucks, meet LinkedIn … with extra foam.
...
"[at Brown University]...“I don’t get the feeling from my classmates that they’re trying to reduce their data footprint.”

Organ donation and transplantation data from around the world

The International Registry in Organ Donation and Transplantation  maintains an informative international database.
Here's a list of the files they show...

WORLDWIDE ACTUAL DECEASED DONORS (PMP) 2013

WORLDWIDE LIVING DONORS (PMP) 2013

WORLDWIDE DCD DONORS (PMP) 2013

WORLDWIDE KIDNEY TRANSPLANT FROM DECEASED DONORS (PMP) 2013

WORLDWIDE KIDNEY TRANSPLANT FROM LIVING DONORS (PMP) 2013

WORLDWIDE LIVER TRANSPLANT FROM DECEASED DONORS (PMP) 2013

WORLDWIDE LIVER TRANSPLANT FROM LIVING DONORS (PMP) 2013

WORLDWIDE HEART TRANSPLANT (PMP) 2013

WORLDWIDE LUNG TRANSPLANT (PMP) 2013

WORLDWIDE PANCREAS TRANSPLANT (PMP) 2013

EUROPE ACTUAL DECEASED ORGAN DONORS (PMP) 2013

EUROPE LIVING ORGAN DONORS (PMP) 2013

AMERICA ACTUAL DECEASED ORGAN DONORS (PMP) 2013

AMERICA LIVING ORGAN DONORS (PMP) 2013

ASIA - OCEANIA ACTUAL DECEASED ORGAN DONORS (PMP) 2013

ASIA - OCEANIA LIVING ORGAN DONORS (PMP) 2013

AFRICA - MIDDLE EAST ACTUAL DECEASED ORGAN DONORS (PMP) 2013

AFRICA - MIDDLE EAST LIVING ORGAN DONORS (PMP) 2013

Data access makes research on schools (and school choice) hard

Courtesy of the Freedom of Information Act, an intrepid reporter lets us in on some of the emails about data access between the Louisiana Department of Education and school choice researchers Parag Pathak and Atila Abdulkadiroglu. These shed light on a controversy and some name calling having to do with a study showing some early problems in Louisiana's school voucher program. The name calling involved accusations that the researchers rushed to publish without waiting for more data. The emails show that they tried unsuccessfully to get more data from the D of E, without success.

Who Gets Access to School Data? A Case Study in How Privacy, Politics & Budget Pressures Can Affect Education Research  by Matt Barnum
"Just who gets access to education data? A case study in La. after a critical early study on school vouchers"

"In the early days of 2016, a study by MIT and Duke University researchers showing the first year of Louisiana’s school voucher program led to marked decreases in student achievement landed in the press and policy worlds with a degree of attention that went beyond the usual wonky provinces of education research.
...
"John White, the state’s high-profile schools superintendent whose pro-school choice policies had long been scrutinized, publicly accused the Duke and MIT researchers of improperly rushing to publish their results and The Wall Street Journal condemned them for similar reasons in an editorial.

The criticism turned on whether the researchers should have waited for additional data before publishing their findings. But even that assertion, it turns out, was complicated. Not long after the headline-grabbing study was released, Louisiana ended its data-sharing relationship with the MIT and Duke researchers, according to emails obtained by The 74 through a public records request.

...

“For a program that’s ongoing, there are real issues of who gets to evaluate the program. Is it open to many teams, which I think is a good model. Or is it restricted to partners?” said MIT professor Parag Pathak, part of the team that studied Louisiana’s voucher program. “There are real broad issues in social science — it’s something that we’re all wrestling with.”

**********

Read it all...you can see why access to data can be hard.

*************

Update: the first comment below points to this critical blog post by Professor Jay Greene:
The Chutzpah of Abdulkadiroglu, Pathak, and Walters

A comment by Christopher Walters points to  this reply by Abdulkadiroglu, Pathak, and Walters:
Statement on Allegations of Academic Fraud by Jay P. Greene
Atila Abdulkadiroglu, Duke; Parag Pathak, MIT; Christopher Walters, UC Berkeley
August 5, 2017

U.S. Renal Data System 2016 annual report (data for 2014)

USRDS 2016 annual report
This year’s report provides data from 2014

highlights from the report include:

• There were 120,688 newly reported cases of end-stage kidney disease, representing a slight increase of 1.1 percent compared to 2013. At the end of 2014, there were 678,383 dialysis and transplant patients receiving treatment for end-stage kidney disease, up 3.5 percent from 2013.
• Among all patients who currently receive hemodialysis, use of an arteriovenous fistula, a surgically created vein used to remove and return blood during dialysis, since 2003, has increased from 32 percent to 63 percent. Arteriovenous catheter use has also declined from 27 percent to 18 percent during this period.
• Medicare spending for beneficiaries ages 65 and older who have chronic kidney disease exceeded $50 billion, representing 20 percent of all Medicare spending in this age group. Total Medicare fee-for-service spending in the general Medicare population increased by 3.8 percent in 2014 to $435.6 billion, with $32.8 billion, or 7.2 percent, of that overall spending accounting for end-stage kidney disease patients. Compared to 2013, the costs of Part D claims and skilled nursing facility care in 2014 grew at the fastest rates of 21 percent and 5.5. percent, respectively.
• Prior to 2013, Medicare spending on hospice care in end-stage kidney disease patients had been experiencing one of the highest rates of growth of any category of Medicare spending, but this spending declined by 6.3 percent in 2014.
• As of December 31, 2014, the kidney transplant waiting list increased by 3 percent over the previous year to 88,231 candidates, of which 83 percent were awaiting their first kidney transplant. With less than 18,000 kidney transplants performed in 2014, the active waiting list was 2.8 times larger than the supply of donor kidneys.

“An interesting note on kidney transplants is a relatively recent initiative called kidney paired donation,” Saran says. “The initiative is aimed at increasing the availability of living donor transplants, and in its simplest form is essentially when two living donors do not match with the respective recipients and decide to perform an exchange whereby the donation goes to each other’s compatible recipient. Kidney paired donation transplants have risen sharply in recent years with 552 performed in 2014, representing 10 percent of living donor transplants that year.”

Data, big and small

Alex Peysakhovich and Seth Stephens-Davidowitz write in the NYT about  How Not to Drown in Numbers, about how you can't always interpret data in a dataset just by looking at the dataset in isolation...

"So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data."

Differential privacy and the market for data, at the AAAS meeting tomorrow

If you are at the AAAS meetings in San Jose tomorrow, and interested in how the new data environment interacts with privacy concerns, you might want to check out this session::

Differential Privacy: Analyzing Sensitive Data and Implications

Saturday, 14 February 2015: 10:00 AM-11:30 AM

Room LL21C (San Jose Convention Center)

To realize the full potential of big data for societal benefit, we must also find solutions to the privacy problems raised by the collection, analysis, and sharing of vast amounts of data about people. As discussed in the 2014 AAAS Annual Meeting session "Re-Identification Risk of De-Identified Data Sets in the Era of Big Data," the traditional approach of anonymizing data by removing identifiers does not provide adequate privacy protection, since it is often possible to re-identify individuals using the seemingly innocuous data that remains in the dataset together with auxiliary information known to an attacker and/or present in publicly available datasets. Differential privacy offers the possibility of avoiding such vulnerabilities. It provides a mathematically rigorous formalization of the requirement that a datasharing or analysis system should not leak individual-specific information, regardless of what auxiliary information is available to an attacker. A rich body of work over the past decade has shown that a wide variety of common data analysis tasks are compatible with the strong protections of differential privacy, and a number of promising efforts are underway to bring these methods to practice. In addition, differential privacy has turned out to have powerful implications for questions outside of privacy, in areas such as economics and statistics. This symposium will discuss these facets of differential privacy.

Organizer:

Salil Vadhan, Harvard University 

Co-Organizer:

Cynthia Dwork, Microsoft Research, Silicon Valley 

Speakers:

Aaron Roth, University of Pennsylvania 
An Introduction to Differential Privacy

Sofya Raskhodnikova, Pennsylvania State University 
Differentially Private Analysis of Graphs and Social Networks

Moritz Hardt, IBM Almaden Research Center 
Guilt-Free Interactive Data Analysis

See more of: Information and Data Technology

What happens when Big Data meets human resources?

That's the question asked by this recent article in the Atlantic by Don Peck, which focuses on using nontraditional data to match workers to jobs, either for hiring new workers or for assigning workers to tasks within a firm.

One of the companies they mention (and one I'm interested in) is Knack, which has created video games which generate apparently useful kinds of data about individuals.

In general, there's a question of what should the inputs be for a matching algorithm, and for many purposes economists have focused on preferences, but 'big data' offers some possibilities for informing preferences and identifying good matches.

(See an earlier related post with links to other articles here: New sources of data for selecting who to hire )

The market for kidneys in Iran

The Iranian economist Farshad Fatemi at the Sharif University of Technology sent me this link to his very interesting working paper The Regulated Market for Kidneys in Iran.

Among other things, it is full of institutional detail and comparisons. Here are a few things that caught my eye.

Comparing total (live plus deceased) kidney donation across countries, per million population, the most recent figures (from 2007) are Iran 27.1%; UK 33.5%; Spain 49.5%; US 54.7%. (His source is the Barcelona-based Transplant Procurement Management Organization, whose international database I have yet to fully explore.)

His description of the market for kidneys in Iran includes the following

"After the donor passes the initial tests, the administrators contact the first patient in the same waiting list as the donor’s blood type [and other components of a match]...
If the patient who is on the top of the waiting list at the moment is not ready for the transplant ..., the next patient will be called... until a ready patient will be found. Then a meeting between the two parties is arranged (they are provided with a private area within the foundation building if they want to reach a private agreement) and they will be sent for tissue tests. If the tissue test gives the favourable result, a contract between the patient and the donor will be signed and they will be provided with a list of the transplant centres and doctors who perform surgery.
When the patient and the donor are referred to transplant centre, a cheque from the patient will be kept at the centre to be paid to the donor after the transplant takes place. The guide price has been 25m Rials (≈ $2660) until March 2007 for 3 years and at this time18 it has been raised to 30m Rials (≈ $3190). This decision has been made because the foundation was worried of a decreasing trend in number of donors.

"In some cases, the recipient will agree to make an additional payment to the donor outside the system; it is not certain how common this practice is, but according to the foundation staff the amount of this payment is not usually big and is thought to be about 5m to 10m Rials (≈ $530 to $1060). The recipient also pays for the cost of tests, two operations, after surgery cares, and other associated costs (like accommodation and travel costs if the patient travels from another city). Insurance companies cover the medical costs of the transplant and the operations are also performed free of charge in state-owned hospitals.
"In addition, the government pays a monetary gift to the donor for appreciation of her altruism (currently, 10m Rials), as well as automatic provision of one year free health insurance, and the opportunity to attend the annual appreciation event dedicated to donors...
"The minimum monthly legal wage for 2007 was Rials 1,830k (later raised to 2,200k for 2008). The minimum payment of Rials 45m is around 2 years of minimum wage. "
...
"[T]o prevent international kidney trade, the donor and recipient are required to have the same nationality. That means an Afghan patient, who is referred to the foundation, should wait until an Afghan donor with appropriate characteristics turns up. This is to avoid transplant tourism. "
...
"the donors are mostly men (Table 7). This can be because of the two facts. Firstly, the ages between 22 and 35; when the donation is accepted; is the fertility age; and women are less likely to be considered as potential donors. Secondly, as we mentioned before since men are supposed as the main breadwinner of the family, it is more likely that they sell their kidneys in order to overcome financial difficulties. Female donors count for around 18% of traded kidneys in our data; it is in contrary with the Indian case where 71% of the sold kidneys were from female donors (Goyal et al. 2002)."

In his sample of 598 transplants (Table 6), 539 were "traded kidneys," 10 "non-traded" and 49 "Cadaver", i.e. the vast majority of kidney transplants were live donor transplants with compensation to the donor.