• Oregon Health Study results, part 1

    Officially I’m on vacation, but I’m taking a break to write briefly about the the NBER-published first set of results from the Oregon Health Study, which are out today. The study has a randomized design and examines the difference in health care utilization and some self-reported outcomes for those on Medicaid vs. the uninsured. It is authored Katherine Baicker, Amy Finkelstein, Jonathan Gruber, Joseph Newhouse, Sarah Taubman, Bill Wright, Mira Bernstein, Heidi Allen and members of the Oregon Health Study Group.

    Just to refresh your memory, here’s what I wrote about that study in August 2010,

    Not since the RAND Health Insurance Experiment (HIE) has there been a randomized controlled experiment of the effect of insurance on health outcomes. Finally, a second one is underway, the Oregon Health Study (OHS). It’s being conducted by Heidi Allen, Katherine Baicker, Amy Finkelstein, Sarah Taubman, Bill J. Wright, and the Oregon Health Study Group who report on the study design in the most recent edition of Health Affairs.

    [T]he Oregon Health Study [is] a randomized controlled trial that will be able to shed some light on the likely effects of [Medicaid] expansions. In 2008, Oregon randomly drew names from a waiting list for its previously closed public insurance program. Our analysis of enrollment into this program found that people who signed up for the waiting list and enrolled in the Oregon Medicaid program were likely to have worse health than those who did not. However, actual enrollment was fairly low, partly because many applicants did not meet eligibility standards.

    Get excited! But don’t get too excited. The study runs through 2010 and no outcome results are available yet.

    Some results are available now. They’ve been reported by Ezra Klein, David Leonhardt, and Gina Kolata. Compared to the uninsured group, those in the Medicaid group:

    • received 30% more hospital care,
    • received 35% more outpatient care,
    • were 15% more like to use prescription drugs,
    • received 60% more mammograms,
    • received 20% more cholesterol checks,
    • were 15% more likely to have had a blood tested for high blood sugar or diabetes,
    • were 45% more likely to have had a pap test within the last year (for women),
    • had lower out-of-pocket medical expenditures and medical debt,
    • had a 40% lower probability of needing to borrow money or skip payment on other bills because of medical expenses,
    • incurred $778 more in spending on health care in one year, a 25% increase over the uninsured mean spending level,
    • were 25% percent more likely to report themselves in “good” or “excellent” health,
    • were 70% more likely to have a usual source of care,
    • were 55% more likely to see the same doctor over time,
    • reported better physical and mental health,
    • were 10% percent less likely to screen positive for depression.
    Klein mentions that “there was no evidence of “crowd-out”: Medicaid coverage didn’t make someone more or less likely to purchase private insurance.” One would hope that all of this would also lead to other objective measures of better health. However, a fuller examination of health outcomes are left for part 2 of the study, for which there are no results yet.

    I expect the research team will find that Medicaid does lead to better health. Such a finding would be consistent with some of the results above (better self-reported physical and mental health, less likely to be depressed, the myriad of higher propensity for preventative tests and treatment). It would also be consistent with a body of other evidence summarized on this blog (see the Medicaid-IV tagged posts) and in a NEJM paper by me, Aaron Carroll, Harold Pollack, and Uwe Reinhardt.

    In looking at the NBER paper, I see that the team used an IV method that addresses any potential bias due to unobservable differences between treatment and control groups. They used the selection by lottery as an instrument. It is clearly unrelated to outcomes but tightly related to placement in treatment and control groups. It’s a perfect instrument and an accepted, proven way to handle contamination or other unobservable differences between treatment and control groups in a ranomized design (see Angrist and Pischke).

    I look forward to reading subsequent work from the Oregon Health Study.

    UPDATE: Removed a paragraph on potential treatment-control bias based on feedback from study authors.

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