As has been said over and over, this study used a large inpatient database to examine the association between primary payer status and outcomes.  And, as has been said, it found that being uninsured or having Medicaid led to worse outcomes, including a higher risk of death.

I’ve read the whole thing now.  If you came looking for me to tell you the methodology is fatally flawed, I will have to disappoint you.  The authors were thorough, careful, and skilled.  Their analysis is complex, well controlled, and uses established methodology.  I do not doubt that their results are robust, nor do I think there is anything major that I would change.

Where Avik and I will likely agree is on the strict recitation of the findings:

Unadjusted mortality for Medicare (4.4%; odds ratio [OR], 3.51), Medicaid (3.7%; OR, 2.86), and Uninsured (3.2%; OR, 2.51) patient groups were higher compared to Private Insurance groups (1.3%, P < 0.001). Mortality was lowest for Private Insurance patients independent of operation. After controlling for age, gender, income, geographic region, operation, and 30 comorbid conditions, Medicaid payer status was associated with the longest length of stay and highest total costs (P < 0.001). Medicaid (P < 0.001) and Uninsured (P < 0.001) payer status independently conferred the highest adjusted risks of mortality.

Patients with private insurance do best.  After controlling for important covariates, Medicaid had the longest length of stay and highest total costs.  I could come up with explanations as to why this is, but – to be honest – it’s irrelevant to the larger point I want to make, so let’s leave that for the moment.  The other key finding were that Medicaid and Uninsured patients were independently found to have higher mortality after adjusting for other factors.  So far, I bet Avik and I won’t quibble.

But one key point, and again it is not huge, is that all the comparisons showing significance were done with private insurance as the reference.  So it is totally correct to say that both Medicaid and Uninsured were significantly worse that private insurance.  But there was no test comparing Medicaid with Uninsured.  So this study did not show that Medicaid patients had a higher mortality than the uninsured.  Yes, the odds ratio was higher for Medicaid than for the Uninsured, but the confidence intervals overlap with respect to mortality, so we can’t make clear distinctions between those two groups.

But, again, I don’t want to spend too much time on that point.  I agree with the larger point that Medicaid patients and the Uninsured both fared much worse than those with private insurance.  I accept the results.

Now on to likely disagreements.

First off let me address this:

Aaron also notes that Medicaid is voluntary: but this is weak support for the implication that Medicaid, in its current form, is the best we can do. In the Vietnam days, some conservatives used to tell liberals to “love [America] or leave it.” I don’t remember liberals being too happy about that. Nor does Medicaid’s voluntary nature mean, ipso facto, that it must be doing some good. Does the voluntary nature of Medicare overutilization mean that Medicare overutilization is a good thing? Most liberal health policy types that I know believe otherwise.

It’s not that I think Medicaid is voluntary.  It’s that I don’t think Medicaid keeps you from seeing doctors you could otherwise see if you didn’t have it.  As I said before, Medicaid patients are able to see physicians that accept Medicaid plus those that they can pay for out of pocket.  I don’t believe this equals “doing some good”.  I do think this equals increasing the available pool of physicians over what they would have without Medicaid.  I think I know what a response might be to this argument, but I hate when people presuppose what the opposition must think, so I’m going to let others make their own arguments as to why this is wrong.

But the larger issue is to what this all means.   I agree that Medicaid is associated with bad outcomes.  I agree that Medicaid is flawed.  I agree that Medicaid could be improved.  But I don’t necessarily agree that Medicaid is the cause of the problem.

I wish I could repost huge tracts of the manuscript, because I think the authors do an excellent job of describing many of the reasons this association might exist.  Almost none of them are causal.  I will summarize as best I can:

• Elective operations were more common with Private Insurance and nonelective were more common in Medicaid and Uninsured patients, pointing to the fact that those populations don’t have the same choices up front.  Elective surgeries usually are planned for and have better outcomes.  Yes, this is controlled for, but still significant (and noted by the authors).
• Patients with private insurance may have better access to higher quality physicians or facilities.  There is a good amount of evidence that surgeons’ experience matters.  I don’t disagree with this.  I do disagree that Medicaid worsens this over being Uninsured because of the argument I made earlier.
• Other differences may exist between these populations that are unaccounted for.  The authors note:

Both Medicaid and Uninsured payer groups had the highest incidence of drug and alcohol abuse. In addition, Medicaid patients had the highest incidence of acquired immunodeficiency syndrome, depression, liver disease, neurologic disorders, and psychoses. Furthermore, Medicaid patients had the highest incidence of metastatic cancer, which likely reflects the combined influence of deficits in access to care, poor health maintenance, and delayed diagnosis resulting in the presentation of advanced disease stage within this population.

• Patients with Medicaid and the Uninsured use the system differently than those with private insurance. More of them get their primary care in the emergency department.  They are more likely to have language barriers or health literacy issues.  They are more likely to be malnourished and have other issues.

Again, though, we can’t know for sure.

So what should we do with this?  If I read Avik’s argument correctly, he thinks Medicaid is fatally flawed and should be replaced with, perhaps, subsidies to buy private insurance.

Now – in theory – that’s not the worst idea in the world.  In fact, it’s what the insurance exchanges in PPACA represent.  I am going to take Avik at his word and go with this a little further.

In a previous post, Avik seemed to endorse giving the poor a lump sum payment and letting them try and get insurance.  I don’t think that’s a good idea.  With individual ratings, insurance for much of the Medicaid population would still be out of reach even with the subsidy he cites.  But in his most recent post, Avik endorses the Swiss system.  Now, that is much more interesting.

The Swiss system has a powerful mandate.  They require everyone to buy private insurance.  They also give out subsidies so that insurance is not more than 8%-10% of anyone’s income.  All insurance companies are mandated to have a “basic” plan with regulated benefits.  This basic plan also must be non-profit.  All insurance is community rated.  The Swiss system also takes a pretty heavy hand with practitioners.  As Uwe Reinhardt notes:

On the surface, the Swiss health system may give the impression of a price-competitive, consumer-directed health care model. However, the heavy government regulation that pervades the entire system—including the health insurance sector—makes it a far cry from the vigorous, price-competitive health care market envisaged by the advocates of consumer-directed health plans in the United States. Some gestures to competition aside, the Swiss system so far has remained mainly a de facto cartel of insurers and health care practitioners who transact with one another in a tight web of government regulations.

I agree that the Swiss system costs less than ours, achieves comparable and sometimes better outcomes, and covers nearly 100% of their population.  If this is the direction Avik thinks is a good idea, then we may agree more than I think.

As I said before, I think debate is good.  And I don’t think there is necessarily a right or wrong here.  Research is, more often than not, baby steps.  Each study adds a small amount to our understanding; each study is only so generalizable.  Avik believes that the research speaks to the harm Medicaid is doing.  I think that the evidence is not so clear.  But I come from the pediatric world, and the research in pediatrics and Medicaid isn’t necessarily the same as the research in surgery, which Avik cites:

As to Carroll’s point about the fact that the uninsured aren’t getting mammograms and colonoscopies, but Medicaid patients are: that isn’t true, because Medicaid patients can’t get appointments to see doctors (see above). If Carroll was right, it would only render even more striking the fact that the uninsured have their cancers detected earlier than do Medicaid patients. With breast cancer specifically (since he brings up mammograms), Medicaid patients were 31% more likely to have late-stage breast cancer than the uninsured. With surgical resections for colon cancer (since he brings up colonoscopies), in a separate study, Medicaid patients had a 27% higher risk of mortality than the uninsured, and a 9% higher risk of surgical complications.

I freely admit I’m not as well versed in the surgical literature as I am in the pediatric literature.  There are only so many hours in the day, and I really do have a full time job.  But I think we are fundametally interpreting this literature differently.  I have trouble understanding the idea that Medicaid patients have more trouble seeing doctors than uninsured patients because of Medicaid.  The pool of physicians uninsured people can see is equal to the ones they can afford to pay out of pocket.  The pool of physicians a patient a on Medicaid can see is equal to the ones that accept Medicaid plus the ones they can afford to pay out of pocket. So the pool of physicians a Medicaid patient can see is actually a superset of those that the uninsured can see.

Of course, it’s entirely possible that those on Medicaid can’t afford to see the same physicians that the uninsured can, because the Medicaid population if often poorer than the uninsured.  That would be a key difference, and not the fault of Medicaid.

This should not be taken as any sign that the studies Avik cites don’t show Medicaid is flawed.  It absolutely is. Not even single payer advocated believe that we should have Medicaid-for-all.  YES, Medicaid is flawed.  It’s state based, so it can run out of money.  It reimburses too little.  It needs to be improved.

But because Medicaid is state based, it can differ in quality from state to state.  It ranges in its benefits from state to state.  The number and quality of docs who accept it differ from state to state.

Contrary to what some believe, I welcome high quality research no matter what the outcome.  The only way we can improve the system is to first find out what is wrong with it.  And so I hope Avik is not talking about me when he says:

Skeptics of the study are setting themselves up for disappointment if they hope that the detailed results will exonerate Medicaid.

I’m not skeptical of this study.  I’m skeptical of all studies until I can personally evaluate their methods.  And I won’t be disappointed no matter what the final outcome because I have no vested interest in Medicaid.  I have a vested interest only in a high quality, cost-effective health care system.

As I have said before:

You can’t ignore [research] because it doesn’t fit your ideology… [w]hen you do science, you agree to accept the results…

That’s how it goes in research.

-Insurance doesn’t equal care.  Insurance can affect how likely you are to get care and how quickly you might get it.  But any study that looks at insurance has to adjust for many, many other variables in order to get the true effect of insurance.

-There is a large body of literature out there on insurance.  A lot of it shows that people with private insurance do better than those with public insurance or those without insurance; that should not be a surprise.  Most people (and most of your docs) would rather have private insurance than Medicaid.  But would you really rather have no insurance than Medicaid?  If so, that is everyone’s right.  Don’t get the Medicaid.  I wager few would make that choice.

-I find it interesting that most of the literature that Avik cites is about surgery.  Surgery is different than other types of care (like emergency care) in that it is harder to refuse.  So it may be that the uninsured are getting care on a compassionate basis.  Few would provide a screening mammogram or yearly colonoscopy to someone uninsured, however, and you would get that with Medicaid.

On the whole, I think the debate is healthy and good.  No one is claiming that Medicaid is perfect, or that we should all just get Medicaid.  There is always room for improvement.  I also don’t necessarily think that Avik is arguing that we should just dump all the Medicaid people on the street, which is what I think some (not Austin) are implying he is saying.

And I’m stopping there.  Were I on the radio, I would be happy to debate this.  But blogging is too asynchronous when I’m joining in so late.  Except for one thing, and here I’m going to take a tiny issue with Avik’s first post.  It was based on (as far as I can tell) a meeting abstract.

I have a long-standing beef with promoting research that is presented in abstract form at scientific meetings.  It makes for great press and lots of splash, but I think it’s a real problem.  So much so that I have refused to participate in media events or press releases that about my work unless it has already appeared in the peer-reviewed literature.  Why?  It’s not rigorously reviewed.  Here is the total amount that we are able to know about the methods of the study Avik cites:

Methods: From 2003-2007, 893,658 major surgical operations were evaluated using the Nationwide Inpatient Sample (NIS) database: lung resection, esophagectomy, colectomy, pancreatectomy, gastrectomy, abdominal aortic aneurysm repair, hip replacement, and coronary artery bypass. Patients were stratified by primary payer status: Medicare (n=491,829), Medicaid (n=40,259), Private Insurance (n=337,535), and Uninsured (n=24,035). Multivariate regression models were applied to assess outcomes.

That’s it.  Was it a good study?  Valid?  How can you tell?

This ticked me off so much as a fellow, that I actually studied it.  Specifically, we looked at abstracts presented at the Pediatric Academic Societies meeting, which is the largest pediatric research meeting.  You can read the full paper, but here’s the abstract:

OBJECTIVE: The validity of research presented at scientific meetings continues to be a concern. Presentations are chosen on the basis of submitted abstracts, which may not contain sufficient information to assess the validity of the research. The objective of this study was to determine 1) the proportion of abstracts presented at the annual Pediatric Academic Society (PAS) meeting that were ultimately published in peer reviewed journals; 2) whether the presentation format of abstracts at the meeting predicts subsequent full publication; and whether the presentation format was related to 3) the time to full publication or 4) the impact factor of the journal in which research is subsequently published.

METHODS: We assembled a list of all abstracts submitted to the PAS meetings in general pediatrics categories in 1998 and 1999, using both CD-ROM and journal publications. In each year, we chose up to 80 abstracts from each presentation format (“publish only,” “poster,” “poster symposium,” “platform presentation”). We chose either 1) all abstracts in each format or 2) when there were >80 abstracts, a random selection of 80 of them. We assessed each selected abstract for subsequent full publication by searching Medline in March 2003; if published, then we recorded the journal, month, and year of publication. We used logistic and linear regression to determine whether publication, time to publication, and the journal’s impact factor were associated with the abstract’s presentation format.

RESULTS: Overall, 44.6% of abstracts presented at the PAS meeting achieved subsequent full publication within 4 to 5 years. There were significant differences between the rates of subsequent full publication of abstracts submitted but not chosen for presentation at the meeting (22.2%) and those that were chosen for presentation in poster sessions (40.0%), poster symposia (44.1%), and platform presentations (53.8%). There were no meaningful differences between the presentation formats in their mean time to publication and their mean journal impact factor.

CONCLUSIONS: PAS meeting attendees and the press should be cautious when interpreting the presentation format of an abstract as a predictor of either its subsequent publication in a peer-reviewed journal or the impact factor of the journal in which it will appear.

I know that can be overwhelming, so here’s the gist.  We looked at a sample of all abstracts sent in to the meeting, and whether they were ever published in peer-review journals.  The first thing I always remind people is that 87% of abstracts that were sent in were presented.  That’s a lot; very few were refused.  So I wouldn’t necessarily assume that just because an abstract is presented, it’s totally valid.  Second, less than 45%of the research presented was published in a peer-reviewed journal in the next four to five years.  So over half of what was presented at the meeting never was “really” published.

I’m not saying the results of Avik’s discussed study aren’t valid.  I’m saying I can’t tell.  And neither can you, without more information.  The peer review for a meeting just isn’t the same as for full publication.  You have less time, different criteria, and almost nothing by which to judge the work.  Ideally, meetings would stop publicizing abstracts as if they were full studies, but neither they, nor the press, seem likely to do so.

In the dark bars up there are the number of drinks the college students thought they would have to drink in order to have the adverse consequences listed. The white bars are the ACTUAL number of drinks they’d have to have.

Most people are fixated on the difference between the black bars and the white bars.  College students are dangerously overestimating the amount of alcohol they need to do stupid things.

I see something else.  There is a shockingly little difference between the number of drinks it takes for the least worrisome of these things (hangover) to really bad things (blackout).  And it even takes less drinks to achieve unwanted sex than a hangover!

Bottom line – when you get to 7 drinks (on average), everything bad starts to happen pretty fast.  It doesn’t take much more than 7.

First up, I want to say something about the NYT piece on the Dartmouth Atlas:

In selling the health care overhaul to Congress, the Obama administration cited a once obscure research group at Dartmouth College to claim that it could not only cut billions in wasteful health care spending but make people healthier by doing so.

Wasteful spending — perhaps $700 billion a year — “does nothing to improve patient health but subjects you and me to tests and procedures that aren’t necessary and are potentially harmful,” the president’s budget director, Peter Orszag, wrote in a blog post characteristic of the administration’s argument.

So what’s the problem?

But while the research compiled in the Dartmouth Atlas of Health Care has been widely interpreted as showing the country’s best and worst care, the Dartmouth researchers themselves acknowledged in interviews that in fact it mainly shows the varying costs of care in the government’s Medicare program. Measures of the quality of care are not part of the formula.

For all anyone knows, patients could be dying in far greater numbers in hospitals in the beige regions than hospitals in the brown ones, and Dartmouth’s maps would not pick up that difference. As any shopper knows, cheaper does not always mean better.

Even Dartmouth’s claims about which hospitals and regions are cheapest may be suspect. The principal argument behind Dartmouth’s research is that doctors in the Upper Midwest offer consistently better and cheaper care than their counterparts in the South and in big cities, and if Southern and urban doctors would be less greedy and act more like ones in Minnesota, the country would be both healthier and wealthier.

But the real difference in costs between, say, Houston and Bismarck, N.D., may result less from how doctors work than from how patients live. Houstonians may simply be sicker and poorer than their Bismarck counterparts. Also, nurses in Houston tend to be paid more than those in North Dakota because the cost of living is higher in Houston. Neither patients’ health nor differences in prices are fully considered by the Dartmouth Atlas.

The mistaken belief that the Dartmouth research proves that cheaper care is better care is widespread — and has been fed in part by Dartmouth researchers themselves.

I’m not sure where to begin.  On the one hand, I take no issue with the NYT pointing out an obvious limitation of the Dartmouth Atlas.  Yes, the data cannot control for all differences in the population, and, yes, that could account for differences.

What I do take exception with is the idea that because the research is imperfect that it is somehow not worthwhile.  That’s ridiculous.  I also think it’s bizarre that the NYT thinks this is such a journalistic coup.  They didn’t notice this before?  Like, when the research was published?  Did they talk to no one at the time?

No research is perfect.  Look up any study I have published, and you will see a section that begins, “Like any research, this study has limitations that warrant consideration.”  I often use those exact words.  All research has issues, and we should consider them as we judge its import.

If we throw out all research that has any limitations, there will be nothing left.  Deal with it.

So, yes, it is possible that there are differences between Houston and Bismark.  This is the same argument that is wheeled out whenever we compare the US to another country.  Why does our system cost more?  Cause we’re different!  Why does our system have middling outcomes?  Cause we’re different!  Why can’t we cover everyone?  Cause we’re different!

Except, when we look at differences that could account for this, we don’t find them.  We don’t smoke or drink more.  We aren’t sicker.  And, as Atul Gawande found, two communities in Texas (which ain’t that different) had wildly different spending with no good reason.  So the Dartmouth data isn’t crazy.

I don’t believe, as the administration does, that you can easily cut wasted care, save tons of money, and have no apparent changes in quality.  It won’t be easy.  I do believe, however, that we spend a ton of money that is wasted and doesn’t give us much bang for the buck.  That’s what the Dartmouth data tells us.  Those that extrapolate it into potentially misguided policy should be the target of our ire, and not the study itself.

Many physicians confronting declining reimbursement from insurers have invested in ambulatory surgery centers, where they perform outpatient surgical and diagnostic procedures. An ownership stake entitles physicians to a share of the facility’s profits from self-referrals. This arrangement can create a potential conflict of interest between physicians’ financial incentives and patients’ clinical needs. Our analysis of Florida data for five common procedures revealed a significant association between physician-ownership and higher surgical volume. Possible remedies include revising federal law to require disclosure of investment arrangements; reducing facility payments to dilute ownership incentives; and reforms (such as accountable care organizations) that discourage an excessive rate of procedures.

And in describing the work in more detail:

The study looked at all patients in Florida who underwent one of five common outpatient procedures: carpal tunnel release, cataract excision, colonoscopy, knee arthroscopy and myringotomy with tympanostomy tube placement (a procedure to insert tubes in the ear).

The researchers determined which doctors were owners of a surgery center. They then compared surgery use among owners in two time periods—before and after they acquired ownership—with that of physicians who remained non-owners.

Results of the study appear in the April issue of Health Affairs. The findings include:

• Owners operated on an average of twice as many patients as non-owners.
• While caseloads increased overall between the earlier and later time periods for all physicians, the increases were more rapid and dramatic among owners.

The number of surgery centers has increased nearly 50 percent over the last decade, largely driven by the investment of physicians, who had a stake in 83 percent of these facilities. For doctors, investment may give them more control over their practice environment, from scheduling cases to purchasing surgical equipment. For patients, these centers often have shorter wait times than hospitals and may provide more amenities.

When they are incentivized to make more money, people – even doctors – are likely to do things that will pay more.  That’s not a moral judgment, but it’s true.  It’s the reason that 25 cent coupons work.

It’s not illegal, but is this a good idea?

Background When copayments for ambulatory care are increased, elderly patients may forgo important outpatient care, leading to increased use of hospital care.

Methods We compared longitudinal changes in the use of outpatient and inpatient care between enrollees in Medicare plans that increased copayments for ambulatory care and enrollees in matched control plans — similar plans that made no changes in these copayments. The study population included 899,060 beneficiaries enrolled in 36 Medicare plans during the period from 2001 through 2006.

Results In plans that increased copayments for ambulatory care, mean copayments nearly doubled for both primary care ($7.38 to $14.38) and specialty care ($12.66 to $22.05). In control plans, mean copayments for primary care and specialty care remained unchanged at $8.33 and $11.38, respectively. In the year after the rise in copayments, plans that increased cost sharing had 19.8 fewer annual outpatient visits per 100 enrollees (95% confidence interval [CI], 16.6 to 23.1), 2.2 additional annual hospital admissions per 100 enrollees (95% CI, 1.8 to 2.6), 13.4 more annual inpatient days per 100 enrollees (95% CI, 10.2 to 16.6), and an increase of 0.7 percentage points in the proportion of enrollees who were hospitalized (95% CI, 0.51 to 0.95), as compared with concurrent trends in control plans. These estimates were consistent among a cohort of continuously enrolled beneficiaries. The effects of increases in copayments for ambulatory care were magnified among enrollees living in areas of lower income and education and among enrollees who had hypertension, diabetes, or a history of myocardial infarction.

Conclusions Raising cost sharing for ambulatory care among elderly patients may have adverse health consequences and may increase total spending on health care.

Here’s the recap.  Some researchers wanted to see what happened to seniors if you increased their Medicare co-pays a bit for primary care and ambulatory care visits.  This is all based on the moral hazard argument.  It goes something like this: People use too much care if it’s free.  So the more you make them pay for it out of pocket the less they will use.  People who need the care the most will pay for it, but people who don’t need it will avoid it.  We become more efficient shoppers, spend less on needless care, and everyone wins.

Right?

No.

What happened here is that just by increasing the co-pay from $7 to $14 and $13 to $22, about 20 fewer outpatient visits occurred per 100 people.  That’s a huge reduction for just a few dollars increase.  Imagine the reduction you would have seen for a significant increase.  And that reduction wasn’t harmless.  There were an additional 2 hospitalizations per 100 people and an average of more than 13 additional days in the hospital.

This minor additional cost-sharing not only resulted in worse health outcomes, but it might also cost more.

Here’s where it gets worse: The most effects were seen in those who were poor or sick.  That’s exactly what we’re trying to avoid.

You will hear some people say this contradicts the findings of the RAND Health Insurance Experiment, which basically “justifies” the whole co-pay thing.  They will say that the RAND HIE showed you can increase co-pays without negative health consequences.  But that’s because many have always misinterpreted the results.  As I’ve argued before about the HIE:

[H]ere’s the gist of that they found: People in the high deductible plans – those most exposed to health care costs – did spend significantly less and consumed less health care.  And, yes, much of that care was unnecessary, as healthy people did not suffer negative consequences  from forgoing care.  BUT, and this is important, poorer participants with hypertension avoided necessary care, and saw their mortality rates rise significantly.

Removing the moral hazard did no harm in the majority of patients (which is touted often as the result of the study) because they were healthy.  And, of course, getting less care when you’re healthy leads to few short term negative results.  But for those who were unhealthy, who comprised a minority of patients in the study, removing the moral hazard led to significant and dangerous consequences.

This study in the NEJM was of elderly people, who were all excluded from the RAND HIE.  They were inherently sicker.  And the results of the RAND HIE for sicker people held.  They fare poorly.  And it might not even save money.

I know it feels like higher co-pays are a good thing.  It seems right to ask people to have more skin in the game.  It looks like it’s fairer and more likely to reduce waste.  But that’s only true for healthy people.  And they’re not who we need to protect.

Outcome Measures The primary outcome was self-report of ever having sexual intercourse by the 24-month follow-up. Secondary outcomes were other sexual behaviors.

Results The participants’ mean age was 12.2 years; 53.5% were girls; and 84.4% were still enrolled at 24 months. Abstinence-only intervention reduced sexual initiation (risk ratio [RR], 0.67; 95% confidence interval [CI], 0.48-0.96). The model-estimated probability of ever having sexual intercourse by the 24-month follow-up was 33.5% in the abstinence-only intervention and 48.5% in the control group. Fewer abstinence-only intervention participants (20.6%) than control participants (29.0%) reported having coitus in the previous 3 months during the follow-up period (RR, 0.94; 95% CI, 0.90-0.99). Abstinence-only intervention did not affect condom use. The 8-hour (RR, 0.96; 95% CI, 0.92-1.00) and 12-hour comprehensive (RR, 0.95; 95% CI, 0.91-0.99) interventions reduced reports of having multiple partners compared with the control group. No other differences between interventions and controls were significant.

Here’s the deal.  They randomized group of young African Americans in 6th and 7th grade to receive one of a number of different types of sexual education.  One centered on “abstinence-only”.  Another group got a “safer-sex” intervention.  Yet another got a combination of the two.  There was also a control group that got none of this.

It’s important that you understand just what was meant by each of these.  So I’m giving you their descriptions (may be behind a paywall for you):

Abstinence-Only Intervention

The 8-hour abstinence-only intervention encouraged abstinence to eliminate the risk of pregnancy and STIs including HIV. It was designed to (1) increase HIV/STI knowledge, (2) strengthen behavioral beliefs supporting abstinence including the belief that abstinence can prevent pregnancy, STIs, and HIV, and that abstinence can foster attainment of future goals, and (3) increase skills to negotiate abstinence and resist pressure to have sex. It was not designed to meet federal criteria for abstinence-only programs. For instance, the target behavior was abstaining from vaginal, anal, and oral intercourse until a time later in life when the adolescent is more prepared to handle the consequences of sex. The intervention did not contain inaccurate information, portray sex in a negative light, or use a moralistic tone. The training and curriculum manual explicitly instructed the facilitators not to disparage the efficacy of condoms or allow the view that condoms are ineffective to go uncorrected.

Safer Sex–Only Intervention

The 8-hour safer sex–only intervention encouraged condom use to reduce the risk of pregnancy and STIs, including HIV, if adolescents had sex. It was designed to (1) increase HIV/STI knowledge, (2) enhance behavioral beliefs that support condom use, and (3) increase skills to use condoms and negotiate condom use. It was not designed to influence abstinence.

And, when all was said and done, the abstinence-only program resulted in fewer adolescents having intercourse in the three months before they did follow-up.  In essence, it resulted in less sex than the other interventions.  It also didn’t result in less condom use, which has been found in previous studies.

You should note that the abstinence only program described above is not the same as many other abstinence only programs.  It didn’t recommend no sex until marriage; it recommended waiting until you are “more prepared to handle the consequences of sex”.  It also was factually and theoretically based.  It also presented data on condoms accurately.  It also did not moralize.

And it worked.

That’s right.  If we’re going to hold with science, then we have to accept the results.  This study showed that this abstinence only program worked.  It should receive further funding and more investigation.  We should consider this type of curriculum as an option to implement.

You can’t ignore it because it doesn’t fit your ideology.

That said, you also can’t extrapolate further than the findings.  This study does not mean that all abstinence-only programs work.  In fact, many other studies have shown that other abstinence-only programs fail.  This was a specific, theory-based abstinence-only program.  It doesn’t vindicate one administration any more than it condemns another.

When you do science, you agree to accept the results.  We should acknowledge that this type of abstinence-only program may have a place in sex education for children in 6th and 7th grade.  And, we should do more research to make sure those results hold in other populations and other settings.  That’s how it goes in research.

Although this does not seem like a fact that most people would commit to memory, somehow the average American has come to know that, not only is it insanely expensive to create a new drug and bring it to market, but it is expensive to the tune of $800 million dollars. Why is this important?  It is the reason most often cited for why medications are so expensive in the United States.  And no one can doubt that they are expensive; we spent over $950 per person on drugs in 2007, and that number is going up every year. Many drug company executives claim that if we didn’t pay so much, then the $800 million dollars needed to create each new drug would prevent us getting any new scientific advances.  Even if you didn’t remember this number, it is reinforced repeatedly in advertisements, news reports, and campaign speeches.  It is so widely accepted that few people challenge the figure, at least not in public.  The truth, however, is a different story.

In order to understand why this is a myth, we must first understand how this number came to be.  In 2001, a group known as the Tufts Center for the Study of Drug Development released the results of a landmark study performed by Joseph DiMasi, MD and colleagues.  They claimed that, after exhaustive work and unprecedented access to company data, the average cost of developing a new drug was $802 million. Unfortunately, the methods of that study were not released until sometime after that, so no one could analyze their figures for over a year. When the paper describing the work was finally released, the $800 million number had already become branded into the American psyche.  So much so that a number of subsequent books and studies attacking the findings not yet been able to change the public consciousness.

So what is wrong with the figure?  A number of things.  First, let’s just approach the study from an economic standpoint.  The actual out of pocket costs for developing a drug in the Tufts study were about $400 million, half of the final number.  How did it get to $800 million then? Through a fancy piece of economic sleight of hand known as “opportunity costs of capital.” You see, when a business chooses to invest money in one area instead of another, it takes a gamble.  The drug industry could spend $400 million on research or, for instance, put it in the bank.  If they put money in the bank instead, they would have earned interest on it.  That interest income was sort of “lost” since they didn’t get to put it in the bank.  Perhaps they could have made even more money if they had invested that $400 million in the stock market.  Or in baseball cards.  The Tufts authors calculated that the drug industry “lost” another $400 million in potential money that they could have earned if they had done something different with the money.  So, they added that up and arrived at $800 million.

The problem with this line of thinking is that research and development is not an investment option for the pharmaceutical industry.  It is a necessary expense — at least it is if they want to stay profitable.  If they want to take their money and invest it in cars that’s fine, but then they are in the auto industry, not the drug industry.  Ironically, when they do their taxes, research is categorized as an expense, not an investment, so any argument about capitalizing costs is rendered moot by the actions of the drug companies themselves.  Research is not a cost to be capitalized.

Which brings us to the other economic trick.  Research is an expense; it is listed as such on tax forms and is, therefore, tax deductible.  This means that the $400 million bill found in the Tufts study was in pre-tax dollars.  Since it is tax-deductible, and the corporate tax rate was about 35%, the actual after-tax cost would be less than $270 million.

While that’s still a lot of money, it is far less than $800 million.  But there are even more problems with the Tufts study.  When drugs go in for approval from the U.S. Food and Drug Administration (FDA), they are classified according to their perceived importance and novelty.  A small percentage of the drugs seeking approval are what are known as “New Molecular Entities” (NMEs.)  Most of the drugs the FDA reviews are just slightly different versions of drugs that already exist on the market.  It makes intuitive sense that it would cost more to make an NME than to merely change an existing drug into a slightly different molecule.  Not only did the Tufts study only look at NMEs, it only included NMEs where the entire cost of development was born by the drug company itself.

As I discussed yesterday, the government, not the drug companies, supports many of the costs of developing drugs. A very, very small number of drugs are developed entirely by industry.  So by taking only NMEs and taking only NMEs that were entirely and solely paid for by companies, the study selected the most expensive drugs for analysis.  This does not yield an “average” cost, no matter what the New York Times says. It provides a cost for the most expensive and rarest types of drugs around — NMEs developed entirely in house.

Even that result is suspect, however.  To this day, no one outside of the study has been permitted to examine the data that the drug companies provided secretly to the Tufts group.  And while we would like to believe the best about everyone, we would be naive to ignore the desire of the pharmaceutical industry to make the costs of research and development seem as high as possible.

Are there any other studies out there to help us arrive at the true cost?  Of course there are.  In 2001, Public Citizen, a consumer safety group known best for its founder Ralph Nader, published a detailed analysis attacking the findings of the Tufts group.  They made a compellingly simple argument: to calculate the average research and development costs to bring a drug to market, you only need to know the total amount spent on research and development and the total number of drugs that obtained FDA approval. Dividing the costs by the number of drugs yields the average cost per drug.

Since total costs are available through publicly available data, and drug approvals are available through the FDA, the group was able to analyze data from 1994 to 2000. They found that the average cost to develop a drug all the way through FDA approval was $161 million pre-tax, and $110 million in after tax dollars. Again, not chump change, but not $800 million.

Even if you think Ralph Nader should not be trusted any more than the drug companies, anyone can reproduce this experiment using more recent data.  According to the 2005 Pharmaceutical Research and Manufacturers in America (PHRMA) Industry Profile, drug companies spent about $121 billion on research and development in 2000-2003. In that same time period, 314 drugs obtained FDA approval. This comes to about $385 million per drug in pre-tax dollars, and assuming a corporate tax rate of 35%, a total of $250 million per drug.

All of this information may leave you with a spinning head. After all, we want the drug companies to spend more time developing NMEs because odds are that most breakthroughs in healthcare will come from these types of drugs.  Far too many new drugs are just copies of drugs that already exist and are really no better than the originals.  It is also likely that NMEs cost more to create than copycat drugs.  However, since NMEs represent such a small percentage of new drugs, it is unfair and dishonest to represent the costs of these drugs as the average costs of new drug development.

Is $250 million a more accurate, and up-to-date, average cost for bringing a drug all the way to market?  Likely, yes.  Some will argue with this.  In fact, Bain & Company, a consulting firm, made headlines by claiming that the cost per drug is really $1.7 billion. However, they argue that the costs of marketing and advertising, which are 2 to 3 times higher than research and development costs, should be included in the cost of creating a new drug.  That’s ridiculous; truly amazing new drugs need no marketing or advertising, only copycat drugs do.

Ultimately, the truth will never be known until the drug companies open up their books and allow us to examine their data ourselves.  That’s not likely to occur soon.

I learned so much of this from Marcia Angell’s The Truth About the Drug Companies: How They Deceive Us and What to Do About It that I’m going to plug it again.  Go buy it.

In the last part of my previous post, I explained how FDA data shows that the vast majority of newly approved drugs are not real improvements over what we already have.  How much work do pharmaceutical companies devote to those drugs in terms of research, however?

In 2001, Darren Zinner published a study in Health Affairs that addressed this very question.  Here’s what he did in plain English.  He looked at all clinical patent applications in 1998, and carefully examined all the scientific research cited in those applications.  It’s important to remember that this would include all research, not just those for approved drugs, so it even includes the research for drugs not getting to market.  He then classified where that research was done.  Here’s what he found:

The majority of research cited in patent applications was done in academic centers.  Some more was done in other non-profit or government research centers.  Only 15% of the research was done by industry.  That’s not a very compelling argument for the indispensable contribution of industry to research.

This work has been repeated in slightly different ways.  In 2001, Public Citizen got their hands on an internal study done by the NIH, where they had looked at the top five selling drugs from 1995.  For completeness sake, these included Zantac, Zovirax, Capoten, Vasotec, and Prozac.  Please note – I’m not disputing the importance of those drugs.  The NIH then looked at the relevant published research for the development of those drugs (and their sources).  Can you guess what they found?

NIH found that “NIH-funded research played a critical role in drug discovery in each of these cases.” In all, U.S. taxpayer-funded researchers conducted 55 percent of the published research projects leading to the discovery and development of these drugs (and foreign academic institutions 30 percent). “Researchers at U.S. universities and at NIH contributed by discovering basic phenomena and concepts, developing new techniques and assays, and participating in clinical applications of the drugs.”

In the case of the hypertension drugs captopril and enalapril, the NIH concluded that the drugs were developed thanks to public U.S. research projects and five foreign academic studies. Only three significant studies were conducted by the drugs’ patent holders, Squibb and Merck.

Furthermore, four of the taxpayer-funded studies were deemed “key” and six of the studies were referenced in the industry’s work. The studies sponsored by the patent holders for these two drugs were of less consequence – none were considered “key” by the NIH. In fact, for the five drugs it studied, the NIH deemed only one industry study “key.” (Public Citizen acknowledges the fact that academics generally have greater incentive to publish research than industry scientists.)

The similarities from the two studies are convincing.  About 15% of research comes from industry.  Over half was from NIH-funded labs.  If you drill down even more, and look only at key papers for discovery or development, only one of the seventeen papers in this category came from industry.  Again, it’s hard to make an argument that the industry contribution is so terribly important that it justifies never reducing the amount we’re spending on drugs at all.

Please not that I am not asking for us to abolish drug companies, nor minimizing their potential importance in certain areas.  As infrastructure for bringing research from the lab to the real world, they do reasonably well.  They also obviously make and transport the drugs well.  But let’s not over-emphasize their importance in research and development – which is always what they do to justify the expense of drugs.

Medical R&D at the turn of the millennium.  Zinner DE. Health Affairs. 2001 Sep-Oct;20(5):202-9.

Rx R&D Myths: The Case Against The Drug Industry’s R&D “Scare Card” by Public Citizen (2001).