The potential cumulative financial hit to patients with costly chronic illnesses is considerable. Those with high spending in at least 5 of the 6 years examined had average annual expenses around $30000. That’s well above typical deductible levels for employer-sponsored plans (usually below $2000) or for Affordable Care Act (ACA) plans (usually in the $1500-$4000 range for silver-rated plans). Unsurprisingly, certain chronic conditions—like rheumatologic conditions, renal disease, diabetes, and AIDS—are associated with high, long-term spending persistence. […]
Patients with high health care costs, particularly those with chronic conditions, are increasingly left underinsured today. A new Medicare initiative could begin to change that. It would permit Medicare Advantage plans in 7 states to reduce cost sharing for proven, high-value therapies targeted to patients with chronic conditions.
Ezekiel Emanuel made the very popular cost shifting argument in today’s New York Times:
Medicare negotiations would do nothing to contain drug prices for the 170 million Americans who have private health insurance, through their employer, the exchanges, or by self-purchase. Having the federal government negotiate lower prices for Medicare would most likely drive up prices on the private side as drug companies tried to recoup their “lost” profits.
The VA purchases drugs at prices about 40% below those paid by Medicare drug plans, which are all private plans. Medicare does not use its price-setting power for drugs the way it does for hospital and physician services. Whether it should is a hot topic today, as it was during the run up to the 2003 law that created the Medicare drug benefit, Part D. The market consequences of a VA-Medicare drug plan would be similar to Medicare drug price setting: it would push the price paid for many drugs well below manufacturers’ claimed costs.
This is why drug manufacturers argue against Medicare drug price setting. Their argument frequently includes the claim that if Medicare (or the VA) pays a lot less for drugs, manufacturers will just shift costs to commercial market payers. Premiums will go up for everyone else. (It’s unclear to me why this should bother drug manufacturers, since, by this logic, they get paid either way, if not from Medicare, the VA, or Medicaid, then from commercial market plans. You’d think they’d want to keep quiet about that.)
I worked this cost shifting argument into my manuscript on a VA-Medicare drug plan. When he saw it, one of my co-authors paid me a visit. “Do you buy this cost shifting argument?” he asked.
“Sure,” I said. “If drug manufacturers are paid less by the VA or Medicare, don’t they need to make up for that lost revenue?”
“Well, do you think drug manufacturers are profit maximizing organizations?” His question was a trap.
“Of course! That’s why they cost shift.” I had just fallen right into it.
“Austin,” he said, “if they could profitably raise prices to commercial market payers after government ones pay less, why didn’t they raise those prices before? It suggests they left money on the table.”
Many privately insured Americans will have an opportunity in the next couple of months to select among several to dozens of health plans. How (not) good are people at choosing among health plan options? In the first of several posts on the AcademyHealth blog, I document just how (not) good they are.
I need to cite this, but haven’t seen it appear anywhere else online. (Come on media!!!)
From Jim Poterba, President of the National Bureau of Economic Research, via email:
Dear “New This Week” Subscribers:
Our practice to date has been to send all recipients of the NBER “New This Week” announcement a message with the new working papers listed in ascending numerical order assigned based on when each paper was ready for distribution. A recent study (http://www.nber.org/papers/w21141) found that papers at or near the top of the list were downloaded more frequently than their lower-listed counterparts.
To avoid inequities across working papers that result from list placement differences, beginning next week, the order of papers in each of the more than 23,000 “New This Week” messages that we send will be determined randomly. This will mean that roughly the same number of message recipients will see a given paper in the first position, in the second position, and so on.
Every so often someone asks me one of the questions I hate: “What is the most important and under-discussed health policy issue right now?” or some variant thereof.
I hate it because of how I’m wired, not because it’s a bad question. The truth is, just about every health policy issue of import right now is a variant of an issue that has been important for decades and isn’t going away anytime soon either, whether under-discussed or not. My brain just isn’t as tuned to what’s hot and under-discussed as to what’s interesting (to me) or important (for one reason or another) all the time.
This got me thinking, just what is it that I find interesting and important all the time? These would naturally be the issues that attract me to whatever papers I pull from the dozens of journal tables of contents I receive each week. Some papers are must reads (to me). Others I pass up.These are also issues I’m very likely to write about again and again.
In no particular order, here’s the list I’ve been making on subject areas I almost always care about, with some notes on whether I think each is broadly important for policy or mostly just interesting to me (because I’m weird):
Effects of payments level and manner on health care quality and outcomes—important and basically a generalization of interest ACOs
Wellness programs, specifically how they affect health and spending—important. Many companies really are interested in or implementing these. They ought to really care what the impacts are.
Consolidation in the health care market (horizontal and vertical) and implications—important
Medicare advantage market, growth, value, costs, including premium support-like reforms—important
Part D market—important
Consumer decision-making in health insurance markets—interesting. I just don’t see results of work in this area binding on policy, but I could be wrong.
The opioid epidemic—important
Hospital or health sector productivity—interesting
Comparative effectiveness, cost effectiveness, and the management of health care technology—important
Observational methods for causal inference/big data—interesting
Labor market and health care (job lock, premium-wage trade-off)—interesting
Consumer directed health plans—important
Quality measurement and incentives—important
Management of and leadership in health care—interesting
Data access for research—important
Any clinical area with which I have personal experience as a patient (insomnia, kidney stones, etc.)—interesting
I probably forgot some things I’m actually interested, and have revealed as much in my posts. If you think so, remind me! Also, maybe you think I should find something not on this list to be important or interesting enough to pay close attention to. If so, what is it? Tell me on Twitter or by email.
The series, by Dr. Lisa Rosenbaum, a cardiologist, challenged readers to consider industry sponsorship in the context of many other sources of bias influencing clinical care, research results and the willingness to support a new drug. Recent analysis suggests industry-sponsored drug and device studies show more favorable results than those sponsored by other sources. Other work showed that Food and Drug Administration advisory panel members with financial ties to a drug company rendered judgments more favorable to it. Other ties to, or gifts from, drug companies, including paid travel to conferences, are associated with greater affinity for and prescribing of the drugs those companies manufacture.
Among these sources of bias, financial conflicts loom large because they are the only potential conflicts that researchers tend to disclose. Disclosing them is appropriate and useful, but what effect do such disclosures have — and what effect should they have — on our interpretation of the studies they accompany?
Very bad. Go read Margot Sanger-Katz for the evidence-based details. For all the reasons she explains, and others, I never understood the appeal of this idea. It only makes sense if you don’t know what you’re talking about.
Either Medicare Advantage is doing something amazing or data limitations are skewing our view of it. I imagine most people’s priors will drive them to interpret the findings of Bruce Landon and colleagues in one of either of those two ways.
Medicare Advantage (MA) plans have greater flexibility than traditional Medicare (TM). MA can offer more benefits, selectively contract with providers, impose utilization controls (like referral requirements), and implement care coordination programs without large regulatory burdens or new acts of Congress. MA plans must also be responsive to the market, which should provide incentives for higher quality and greater efficiency.
Put it all together and, in theory at least, MA should outperform TM in efficiency and quality. But does it?
Most studies fail to convince one way or the other because researchers are not permitted the same degree of access to MA data as that for TM. For the latter, full claims over many years are available* (though quality measures not derived from claims data are not). For the former, some aggregate measures of utilization provided by plans are usually all we get, and when we get them, they’re not over many years. (However, more quality measures are available from MA plans.)
Comparing MA to TM is like trying to compare two houses, one of which you can live in, the other of which you can only observe through a few keyholes.
In 2006 and 2007 (and only 2006 and 2007), the Centers for Medicare and Medicaid Services (CMS) offered a glimpse of MA through a new keyhole: relative resource use (RRU) data. These plan-level data measure utilization with standardized prices, which removes geographic and MA- or TM-specific price differences. They do so for diabetic patients in both years and those with cardiovascular disease in 2007 only. They are also stratified by age, sex, diabetes type (1 or 2), cardiovascular disease (acute myocardial infarction, congestive heart failure, angina, or coronary artery disease), and the presence or absence of at least one major comorbidity.
Individual-level Healthcare Effectiveness Data and Information Set (HEDIS) data for MA plans—which measure quality of ambulatory care—are also available for those years (and many others). Using 2007 RRU data to measure efficiency and HEDIS to measure quality, Landon et al. constructed similar resource use and quality metrics for a 20% random sample of TM beneficiaries. Quality metrics included, for diabetics, A1C testing in the current year and a diabetic retinal exam in the current or prior year; for both diabetics and patients with cardiovascular disease, LDL cholesterol testing in the current year. These quality metrics are only applicable to and computed for 65 to 75 year olds.
To control for geographic variation in service delivery and quality and demographic differences between MA and TM, the authors weighted the TM sample such that it matched their MA sample demographically within each zip code. This also controls for zip code level socioeconomic differences across the two samples.
On average, RRU was about 20 percentage points lower for MA than TM. Lower utilization was observed in MA across both disease types and service categories (inpatient, surgery and procedures, evaluation and management). However, as shown in the figure below, for newer (entered the program in 2006 or 2007), smaller (<25,000 enrollees), and for-profit HMO or PPO MA contracts,** RRU was higher in MA than TM for inpatient care.
The chart below combines resource use and a composite of diabetes care quality for MA HMOs vs TM. (A chart with similar patterns for cardiovascular disease is provided in the paper’s appendix.) The former is on the horizontal axis (low spending to the left, high to the right). The latter is on the vertical axis (low spending downward, high upward). Each data point (circle or triangle) is the difference between a specific HMO contract and TM. Larger symbols are for larger contracts (>25,000 members); triangles for new contracts, circles for older ones; blue for nonprofit and purple for for-profit.
By and large, MA HMOs use fewer resources and provide better quality, though this is more often the case for larger, established ones (relatively more big circles in the upper left and relatively more small triangles in the lower right). (The authors did not include a similar analysis of PPOs. They wrote me that most PPOs were small, new, and for profit, and there were many fewer of them than HMOs in 2007.)
The authors point out several limitations of the analysis:
It only considered a few aspects of quality, as constrained by data availability.
It is possible MA plans experienced favorable selection in the time period assessed, even within disease type, and even controlling for demographics, comorbidities, and socioeconomic status, to the extent the authors could and did.
The data are quite old, from 2007; that’s the latest year available.
To these, we should add:
Only two disease types were considered, again because of data limitations.
It is possible MA plans upcoded relative to TM such that the MA cohort appeared relatively sicker, which after adjustments, might make resource use look relatively lower.
Beneficiaries with “concomitant specified dominant medical conditions including active cancer, end-stage renal disease, human immunodeficiency virus/AIDS, and organ transplants” were not included in RRUs and, hence, excluded from the analysis. It’s possible MA plans provide disproportionately inefficient or poor care to such beneficiaries.
The analysis included all ages above 65 but the contract HEDIS quality measures used in the analysis are only applicable and computed for ages up to 75, so it’s possible there are some offsetting quality differences for older enrollees.
Within zip code socioeconomic differences could not be controlled for.
PPOs were excluded from the quality/efficiency analysis (the figure just above).
Even if the results accurately depict the efficiency and quality of MA, relative to TM, it must be emphasized that MA plans were paid well above their costs in 2007 and are still paid above them today, though not by as much. In other words, whatever their efficiency, taxpayers are not benefiting; whatever their quality, that comes at a higher price.
Still, either MA is doing something amazing—broadly providing substantially better care with less utilization, which is something few initiatives in TM have ever been able to do—or the results are (or are in part) artifacts of analytic limitations. The best way to decide which is to do more research with more complete data. Until we’re offered more than selected glimpses through keyholes at MA, we may never get the chance to do that.
Austin and Aaron are participants in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to amazon.com.