Innovations in Health Care
The latest issue of the journal Innovations focuses on health care and is excellent. It’s a very special issue–a double Tabarrok issue!
My paper, Operation Warp Speed: Negative and Positive Lessons for New Industrial Policy, asks what can learn from the tremendous success of OWS about an OWS for X? What are the opportunities and the dangers?
My son Maxwell Tabarrok’s paper is Peptide-DB: A Million-Peptide Database to Accelerate Science. Max’s paper combines economics and science policy. Open databases are a public good and so are underprovided. A case in point is that there is no big database for anti-microbial peptides despite the evident utility of such a database for using ML techniques to create new antibiotics. The NIH and other organizations have successfully filled this gap with databases in the past such as PubChem, the HGP, and ProteinDB. A million-peptide database is well within their reach:
The existing data infrastructure for antimicrobial peptides is tiny and scattered: a few thousand sequences with a couple of useful biological assays are scattered across dozens of data providers. No one in science today has the incentives to create this data. Pharma companies can’t make money from it and researchers can’t produce any splashy publications. This means that researchers are duplicating the expensive legwork of collating and cleaning all of this
data and are not getting optimal results, as this is simply not enough information to take full advantage of the ML approach. Scientific funding organizations, including the NIH and the NSF, can fix this problem. The scientific knowledge required to massively scale the data we have on antimicrobial peptides is well established and ready to go. It wouldn’t be too expensive or take too long to get a clean dataset of a million peptides or more, and to have detailed information on their activity against the most important resistant pathogens as well as its toxicity to human cells. This is well within the scale of the successful projects these organizations have funded in the past, including PubChem, the HGP, and ProteinDB.
Naturally, I am biased towards Tabarrok-articles but another important paper is Reorganizing the CDC for Effective Public Health Emergency Response by Gowda, Ranasinghe, and Phan. As Michael Lewis wrote in The Premonition by the time of COVID the CDC had became more akin to an academic department than a virus fighting agency:
The CDC did many things. It published learned papers on health crises, after the fact. It managed, very carefully, public perception of itself. But when the shooting started, it leapt into the nearest hole, while others took fire.
Gowda, Ranasinghe, and Phan agree.
The COVID-19 pandemic revealed significant weaknesses in the CDC’s response system. Its traditional strengths in testing, pathogen dentification, and disease investigation and tracking faltered. The legacy of Alexander Langmuir, a pioneering epidemiologist who infused the CDC with epidemiological principles in the 1950s, now seems a distant memory. Tasks as basic as collecting and providing timely COVID-19 data, along with data analysis and epidemiological modeling—both of which should have been the core capability of the CDC—became alarmingly difficult and had to be handled by nongovernmental organizations, such as the Johns Hopkins University Coronavirus Resource Center.
A closer examination of the CDC’s workforce composition reveals the root cause: a mere fraction of its employees are epidemiologists and data scientists. The agency has seen an increasing emphasis on academic exploration at the expense of on the-ground action and support for frontline health departments. (Armstrong & Griffin, 2022).
The authors propose to reinvigorate the CDC by integrating it with the more practical and active U.S. Public Health Service. This is a very good suggestion.
For one more check out Bai, Hyman and Silver as a primer on Improving Health Care. The entire issue is excellent.