The January 2010 issue of PLoS Medicine contains four interesting articles on the "global health system" The articles define what this system is, discusses the role of nations within this larger system and talks about how to strengthen it's effectiveness. These pieces are written by leaders in the field and provide some concrete discussion on a concept that has gained currency over the last few years, that global health is a policy domain of importance and that nations can be thought interlinked as part of a larger "health ecology" when developing policies to address morbidity and mortality the world over.
For more on global health and global health governance, you may want to check out this interview of one of my thesis committee members, Yale University Professor Jennifer Prah Ruger. Her work (linked here) focuses on this broader global health system, linking insights from ethics, politics, policy and economics to understand how and when investments in international health are and should be made.
Finally, for a topical piece on health care worker shortages worldwide, and the role of the United States in alleviating these, see this great essay by Yale medical student (and my former roommate!) Dayo Fadelu.
Tuesday, February 16, 2010
Monday, February 15, 2010
Non-Technical Introduction to Causal Inference/Methods
Hi everyone, the blog is back in business.
I recently came across a great working paper seeking to introduce econometric methods geared towards understanding causality to a non-expert audience. I'm particularly excited about this because I think many of these methods could really be useful in medical care/clinical questions where it is either unethical or technically difficult to randomize patients (and yes, there are still plenty of those!). For whatever reason, these methods are, in my estimation, rather underutilized in medicine.
Obviously, while the linked piece is geared towards education policy, the methods can be used in any context. Here is the abstract:
Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition of leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-in-differences, and panel-data techniques. Illustrating applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for disadvantaged, and compulsory-school and tracking reforms.
Enjoy!
I recently came across a great working paper seeking to introduce econometric methods geared towards understanding causality to a non-expert audience. I'm particularly excited about this because I think many of these methods could really be useful in medical care/clinical questions where it is either unethical or technically difficult to randomize patients (and yes, there are still plenty of those!). For whatever reason, these methods are, in my estimation, rather underutilized in medicine.
Obviously, while the linked piece is geared towards education policy, the methods can be used in any context. Here is the abstract:
Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition of leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-in-differences, and panel-data techniques. Illustrating applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for disadvantaged, and compulsory-school and tracking reforms.
Enjoy!
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