• My other favorite health policy charts of 2013

    If you haven’t seen it yet, you should go check out Matt O’Brien’s catalogue of the 41 most important policy stories of 2013—in charts. I offered up the graph on early enrollment trends in Massachusetts; last month I teased out its implications more fully in a post titled “This chart should be getting more attention“.

    I actually sent Matt three of my favorites to choose from. Since only one could make the cut, I figured I’d share the other two here.

    Contrary to popular belief, there isn’t much young-subsidizing-the-old going on in Obamacare: The fact that young, healthy people are seeing pre-subsidy increases in health insurance premiums is often misattributed to the restriction on age-rating (insurers only being allowed to charge their oldest beneficiaries three times what they charge the youngest). But relaxing the age band would have little impact on premiums for young adults. The increases are primarily being driven by prohibiting insurers from discriminating along health status and requiring more comprehensive floor for minimum benefits (Austin and I wrote a Bloomberg op-ed on this, here). Oh, and this chart is pre-subsidy.

    agebands

    Study: Why the ACA’s Limits on Age-Rating Will Not Cause “Rate Shock”: Distributional Implications of Limited Age Bands in Nongroup Health Insurance by Linda Blumberg and Matthew Buettgens (Robert Wood Johnson Foundation, 2013)

    The red dotted line represents the variation in premiums that would be expected if age rating varied by the average covered expenses of those individuals actually expected to enroll in nongroup coverage under the ACA. The 3:1 age gradient developed by CMS is reasonably consistent with expected enrollee expenses, particularly for those up to age 27 and for those age 42 and older. Using the 5:1 age gradient would tend to undercharge young adults relative to their actual expenses and overcharge older adults relative to their actual expenses.

    Health outcomes in the United States are depressingly bad: Aaron has covered this beat far more thoroughly than me (see here, here, and here, just to start), but I think this chart is one of the more elegant depictions of “best in the world, my ass.” Despite having the highest health spending of any nation in the world, we fare very poorly when it comes to actual health outcomes. And it’s not just that we’re bad—we’re declining. This chart shows the probability of women in the United States surviving to age 50, compared to 21 peer nations. And no, the gap isn’t entirely attributable to neonatal mortality or violence/accidents—you’ll find a more thorough discussion of the chart and the differential causes of mortality in an old Project Millennial post of mine.

    femalessurvivalto50

    Study: U.S. Health in International Perspective: Shorter Lives, Poorer Health (Institute of Medicine, 2013)

    Although the United States has shared in these improvements, it still forfeits the most years of potential life before age 50. […] This mortality gap has also grown significantly over time. In 1990, U.S. females and males lost approximately 35 percent more years of life before age 50 than did those in other high-income countries, but by 2009 this figure had grown to nearly 75 percent.

    Adrianna (@onceuponA)

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    • Regarding the 2nd chart: Let me preface my comment by saying that I agree completely with the underlying point, i.e., that US healthcare can hardly be described as the best in the world. Nevertheless, I strongly suspect that, in developed countries, the probability of survival to age 50 is far more dependent on social and public health determinants than it is on the health care system. The health care system likely explains a much larger proportion of variability in survival to 85 (for example) among those who have reached 50.

      Are you aware of data that clarify the relative contribution of the health system to survival at different stages of the life cycle?

      • I’m not aware of any charts illustrating what you’re looking for—but I do touch on your point in the original post I wrote about that graph (linked above), which I first saw at a conference. Excerpt:

        A key point: one of the panelists asked the audience, quasi-rhetorically, how many thought health care delivery reform would solve all of these problems. Not a single hand was raised. A thoughtful discussion of social determinants of health ensued.

        An audience member later turned the question back on the panel, during the session’s Q&A. He asked how many of them believed health care could offer nothing to improve mortality trends. Again, not a single hand. It’s some food for thought.

    • For the first chart, looking at the actual data, what is going on at age 53? It looks like the expenses go up sharply from age 48 to 53, then flatten right out. Is that all the 50-year-olds getting mammograms and colonoscopies for the first time?

      The actual data for this graph came from simulating actual expenses under the new ACA benefits. The simulation seems oddly spiky.

    • I’ve seen the report from RWJF on age-band compression not leading to rate shock. I’m a bit confused here. If young people tend to be healthier and older people tend to be sicker, how you can decouple the age-band compression from the guaranteed-issue effect on rate shock? You’d have to be arguing that insurers pre-Obamacare were undercharging the young in the individual market it seems.

      Moreover, if age-band compression leads to savings for the elderly (as the chart indicates), it seems that there should be a concurrent transfer from elsewhere on that chart to compensate for those savings.

      Am I missing something?

    • If the purpose is to actually understand what’s driving mortality differentials, between the US and other countries, it would help to have definitive data regarding what’s driving mortality differentials within the US.

      There are massive and persistent differentials between states, and you can continue to find them at the county level, and even within the hospital catchment areas. It’s no secret that there are also massive differentials between various racial, ethnic, and class cohorts – even when they live in the same county and are served by the same “system.”

      The study linked below isn’t perfect, but at least it’s an honest attempt to discover what’s actually driving mortality differentials.
      http://www.healthmetricsandevaluation.org/sites/default/files/policy_report/2013/IHME_GBD_US_FINAL_PRINTED%20070513.pdf

      If and when the health policy community understands precisely what percentage of the life expectancy differential between black males and asian women, or between poor white men and affluent white men, etc in the US can be explained by differences in health system performance rather than habits and differentials, these charts will continue to tell us less than nothing about differences in health system performance.

      Once in a while, anyone who is making a living as a “health policy analyst” should on the pith helmet and venture out to actual clinical settings and follow real patients from different demographic cohorts have been conducting their lives, respond to clinical directives, etc and think critically about precisely how much “health system performance” has to do with variations in longevity – then see if they still believe they retain their faith in the utility of statistical regressions of this kind.

    • For those that are interested in a state-by-state, contextualized look at the vast disparities between federal and state enrollment figures check out this graph:

      https://twitter.com/colinb1123/status/410914436338036736/photo/1