What’s the “right” way to decompose health spending growth? That’s a trick question. To find out why, read my new post on the AcademyHealth blog.
What’s the “right” way to decompose health spending growth? That’s a trick question. To find out why, read my new post on the AcademyHealth blog.
For many years, researchers and industry observers have conjectured that rising generic penetration might have an impact on the rate and direction of pharmaceutical innovation. Using a new combination of data sets, we are able to estimate the effects of rising generic penetration on early-stage pharmaceutical innovation. While the overall level of early-stage drug development has continued to increase, generics have had a statistically and economically significant impact on where that development activity is concentrated and how it is done. In the full sample, we find that, as our baseline measure of generic penetration increases by 10% within a therapeutic market, we observe a decrease of 7.9% in early-stage innovation in that market. This implies that drug development activity is moving out of markets where generic competition is increasing and into domains where it is relatively less intense.
That’s from the conclusion of a new NBER working paper by Lee Branstetter, Chirantan Chatterjee, Matthew Higgins. Because I am not expert in this area, I have almost nothing of value to add, and, in part for that reason, I did not read the paper in full. However, what I did read was very well written and interesting. It’s worth your time if you’re seeking an introduction to pharmaceutical development and patenting, for example.
The conclusion offers some speculation about welfare effects. They’re good and nuanced, but somewhat limited, as they are focused on the effects of shifts in pharmaceutical innovation only. They did not include the fact that cheaper generics are, by themselves, a welfare gain to consumers though, possibly, a welfare loss to producers. Do these offset? At some degree of substitution of cheaper generics for less (or different) innovation we ought to be indifferent, if the latter is a net welfare loss and the former is a net welfare gain. Maybe some losses or changes in innovation are efficient for this reason. I don’t know.
In any case, maybe I’m not thinking about this correctly. But the fact that it entered my thoughts at all suggests that the authors might want to address it in a future draft. Or maybe they did so somewhere in the middle of the paper and I missed it. As I said, I did not read the whole thing.
In an economic letter from the Federal Reserve Bank of San Francisco, Jeffrey Clemens, Joshua Gottlieb, and Adam Shapiro make the case that Medicare cuts in rates paid to hospitals induce private insurer cuts. They focus on the 2% reduction in Medicare payments after April 1, 2013, as required by the Budget Control Act of 2011 (sequestration).
Here’s the key chart:
Consistent with Figure 1, we see that Medicare price inflation dropped sharply in April 2013—2.5 percentage points between March and April 2013. Over the subsequent year, private-sector price inflation declined during the months associated with substantial numbers of contract renegotiations. Specifically, private PPI inflation fell 0.6 percentage point in July 2013 and 1.6 percentage points in January 2014. These facts suggest that Medicare’s payment cuts systematically passed through into the private payment system.
One should not be impressed with some speculative chart reading. Are there any studies, with stronger methods, that support the idea that private prices paid to hospitals fall when Medicare’s do so?
A study of this relationship in the hospital setting by White (2013) estimates that a 10% reduction in Medicare’s hospital payments results in a 4 to 8% reduction in private payments. White and Wu (2013) further find that hospitals handle these cuts by reducing their operating costs; this and related findings are summarized in Frakt (2013).
In the context of physician payments, Clemens and Gottlieb (2013) estimate the effects of changes in Medicare’s regional payment adjustments. They find that a $1 reduction in Medicare’s payments results, on average, in a $1 reduction in private payments. Since the average private payment exceeds the average Medicare payment by 40% in their study, the results imply that a 1% reduction in Medicare payments reduces private payments by about 0.7%.
The letter goes on to discuss the timing of private sector price responses to Medicare payment cuts. Evidence suggests it’s spread out over many years. In this way, even a one-time cut to Medicare payments can suppress health care prices more broadly and for longer than one might expect. Click through for the details.
Health spending growth is crowding state and local spending in other areas of need. But Medicaid expansion is not to blame. What is? Click to read the answer in my post on the AcademyHealth blog.
In an ideal health care system, you’d get the same (very good) care whether you were admitted to a hospital on a Monday, Wednesday, Friday, or Sunday. We don’t have an ideal health care system, and it turns out that day of admission matters. A new paper by Ann Bartel, Carri Chan, and Song-Hee Kim illustrates this fact, and then exploits it as an instrumental variable (IV) in an analysis of mortality and hospital readmissions.
Prior work by Varnava et al. (2002) and Wong et al. (2009) showed that hospitals would rather not keep patients over the weekend if they can discharge them on a Friday. Examining three hospitals in the UK, Varnava et al. found that discharges were most common on Fridays. Considering a hospital in Toronto, Wong et al. found that “[w]eekend discharge rate was more than 50% lower compared with reference rates whereas Friday rates were 24% higher. Holiday Monday discharge rates were 65% lower than regular Mondays, with an increase in pre-holiday discharge rates.”
Bartel, Chan, and Kim found something similar among US Medicare patients hospitalized for heart failure (HF), pneumonia (PNE), or acute myocardial infarction (AMI) in 2008-2011. The following chart from their paper plots the logarithm of length-of-stay (LOS) versus admission day-of-week for HF patients, controlling for age, gender, race, comorbidities, receipt of surgery, enrollment in Medicare Advantage, seasonality, and hospital fixed effects. (That’s why the figure’s caption calls this a “residual.”) As shown, HF patients admitted on Sunday-Tuesday have shorter lengths of stay than those admitted on a Wednesday-Saturday. A similar pattern exists for PNE and AMI patients.
Why? The hypothesis is that there is an incentive to get patients out of the hospital before the weekend, unless it’s pretty clear they’ll need to stay through the weekend. This could be due to patient demand (e.g., they want, or their family wants them, to be home on weekends). Or it could be due to provider factors (e.g., less staff on the weekend makes it harder for the hospital to provide care or plan discharges). Also, under diagnosis-based payment that Medicare uses, staying an extra day that could be avoided is all cost for no additional revenue.
Whatever the reason if the admission day is random with respect to outcomes, it could be a good instrument, a way to estimate a causal relationship between length of stay and things like mortality or hospital readmissions. If admission day is a good instrument, stratifying by it should balance observable factors, like comorbidities. If, for example, patients admitted earlier in the week are also sicker, then their outcomes could be worse not because they are discharged earlier (before the weekend) but because of their more severe illnesses, invalidating the instrument.
In principle it isn’t absolutely necessary that observable factors like comorbidities be balanced across values of the instrument, because they can be controlled for. However if there is not balance across instrument strata among observable factors, it should reduce our confidence that there is balance among unobservable factors, which is the key hypothesis for IV. So, checking balance on observables, like comorbidities, is a falsification test, something every IV study should include. (If one’s theory suggests that there ought not be balance on some, specific observables, then we might forgive that, and the analysis should control for them. But there must be some observables for which balance occurs or else why should we believe it does so for all unobservables correlated with outcomes?)
This falsification test is a direct analog of the typical “Table 1″ in a publication of results from an RCT. A standard table 1 shows balance of observable factors across treatment/control arms. If you ever saw an unbalanced Table 1, you’d suspect a breakdown in the randomization. The study would be fatally flawed. Well, one can and should do this type of test with IV too.
Considering HF patients, Bartel, Chan, and Kim do find balance of comorbidities when stratified by Sunday/Monday admissions versus admissions on any other day, but only for those with greater severity of disease. The reason could be that day of admission is more random for high severity patients; they may have less control over when they enter the hospital than other, less severely ill patients, the relatively sicker* of whom seem disproportionately to be admitted on Sundays and Mondays. Therefore, their instrument is probably not valid for less severe HF patients. A similar falsification test did not reject the validity of the instrument for the AMI and PNE study cohorts.
Main lesson: Do falsification tests. Adjust analysis accordingly.
The paper’s principal results are as follows:
These results suggest that discharges to avoid weekends and that shorten LOS harm patients, as does shorter LOS in general (at the margin examined). However, we should only believe them to the extent we believe the instrument. The falsification tests in the paper should increase our confidence in the validity of findings.
* To help you parse this: I’m talking about the relatively sicker among the less severely ill subset. This is bloggerrifically vague, but details are in the paper.
Ultrasound is better, but CT scans are the norm for suspected kidney stones. Here’s a streamlined version of the American College of Physicians summary of a new study that backs this up.
The study included 2,759 patients who presented with suspected cases of kidney stone to 15 geographically diverse academic hospital EDs, 4 of which were “safety net” hospitals. Patients were randomized to 1 of 3 groups: point-of-care ultrasonography performed by an emergency physician, ultrasonography performed by a radiologist, or abdominal CT. [...]
The study found a 0.4% rate (11 patients) of high-risk diagnoses with complications within 30 days, and this did not vary significantly by imaging method. [...]
The mean 6-month cumulative radiation exposure was significantly lower in the ultrasonography groups than in the CT group (10.1 mSv and 9.3 mSv vs. 17.2 mSv;P<0.001). The radiation in the ultrasound groups resulted from some patients going on to have additional testing, some of which included CTs. Median length of stay in the ED was significantly longer in the radiology ultrasound group: 7.0 hours compared to 6.3 hours in the ED ultrasound group and 6.4 hours in the CT group (P<0.001 for radiology versus each of the other 1 groups). Return ED visits, hospitalizations, and diagnostic accuracy did not differ significantly among the groups. [...] There was no significant difference in results between those with and those without complete follow-up.
The authors emphasized the results do not suggest that patients undergo only ultrasound imaging, but rather that ultrasonography should be used as the initial diagnostic imaging test, with further imaging studies performed at the discretion of the physician.
The study is here and an accompanying editorial is here. The next time I go to the ED with a suspected kidney stone—if there is a next time—I intend to bring both these papers with me, or pull up this post on my phone. A stone maker shouldn’t die of cancer induced by CT studies for treatment of stones. That would certainly not be “doing no harm.”
In frustration, I storify-ed some tweets about this.
People often ask me how I “do it all.” I think they mean all the blogging, on top of my regular job as a researcher. The simple answer is, I work a lot, much of it in short intervals of time away from my office.
But I very much doubt I work more than the average person who asks, “How do you do it all?” It’s just that a substantial amount of my work product is highly visible: the blogging. I think that gives the impression that I’m doing more in less time.
For all that, I may, in fact, manage time well, as I’ve been told by others for years. People have asked me for time management tips since I was in high school. As has Tyler Cowen and some of the “most productive people on the planet”, I’ve written some down for you below and in no particular order. These are just some aspects of how I generally work and live, only some of which may enhance my productivity.
Two final points: Information in any form (reading, TV, podcasts/radio, the content of meetings, emails, and so forth) is almost entirely entertainment, with little lasting informational value. How much do you recall from a book you read three years ago, a movie you saw one year ago, an hour-long conference call you were on last month, an article you read last week, or a radio program you listened to three days ago? How much can you write down about it? What was the key point or message? With few exceptions, what took many minutes or hours to consume has been converted to, at most, a few sentences of information in your long-term memory. The rest of the information is not retained. From a long-term perspective, most of what you consumed was filler, momentary entertainment (if that), packaging, art, which is all fine and good, but not necessarily memorable information. For gathering information of long-term value, skipping or skimming the likely non-memorable parts and finding ways to codify in a searchable form the important, new information is more efficient, though not necessarily easy. (If one is seeking entertainment, inspiration, and the like, this is not applicable. I like art too!)
Finally, there are many other ways to be productive and types of productive people. Some of my very productive cobloggers work in very different styles, for instance. It leads me to suspect that one is not productive because of one’s methods, but one is simply productivity-oriented first and then develops personalized methods to suit.
The study is by Mehrdad Roham and colleagues:
We find that both the overall volume of services provided per capita and the average cost of these services decreased over our data period, once account is taken of changes in the age distribution of the population (the calculations relate to an age-standardized population) and in prices (all fees are expressed in constant dollar terms, using the consumer price index). However, these decreases are concentrated in services that have low HTI [Health Technology Intensity] and, to a lesser extent, medium HTI; over the same period, the average (age-standardized) number of services for high HTI increased by 55 percent and their share by 7.4 percentage points. We find also that whereas the decreases in the volume and cost of low and medium HTI services took place fairly uniformly across all age groups, the increases in high HTI were concentrated in the middle age groups and, more especially, in the old age groups.
The results suggest two main policy implications. First, technological change and its diffusion within the population are too important to ignore: decision makers (and the policy discussion) should focus on how the delivery of care is changing while, at the same time, accounting for the effects of external changes (such as population aging). Second, health technology assessment should be based on real-life ex-post studies of how health technologies are used by doctors and patients rather than one ex-ante studies of how they should be used. That would help health policy analysts and researchers to gain a better understanding of the relationships between aging populations and the relative distribution of spending on health care for different levels of health technological intensity. Taking into account the observed changes in the use of technology in relation to patient age will also help to produce better predictions of future health care expenditures. However, the important questions of whether the observed changes are warranted, in the sense of leading to better patient outcomes and being cost effective, are ones that we are not able to address. It would be of great analytical and policy interest to have records that include information about patient outcomes following procedures, and not just the procedures themselves.
The bit in bold (added) is a key point that many overlook. Many look to new technologies to cut costs and improve outcomes. That’s how they’re marketed. And, they very well may do so if their use is restricted to the subset of the population for which they’re ideally suited and designed. But what is typical is that technology diffuses more broadly than efficient use would warrant, in part because it’s good business. That ends up turning valuable technology into waste (or, more accurately, valuable for some, wasteful for others). And this is why I’m deeply skeptical of claims that any technology will actually cut costs and improve outcomes, on average, even if it does so for some.