Something Not Unlike Research

US mass incarceration as a public health problem

post by Bill Gardner

The US imprisons more of its citizens on a per-capita basis than any other country in the world. The Bureau of Justice Statistics reported that in 2008 there were about 1.6 million US prisoners.

Consider incarceration as a health risk exposure, in the epidemiological sense. What are the consequences?

Dora Dumont and her colleagues review the evidence about how this affects the health of the imprisoned, and the rest of us. It was not a surprise to discover that inmates have substantially worse health than age and gender-matched persons who are not incarcerated. They are five times more likely to be HIV+ and nine times more likely to be infected with hepatitis C than the rest of us. They have far higher rates of mental disorders. Many inmates suffer injuries or death from physical or sexual assaults.

What was a surprise is that, despite this,

At first sight, incarceration appears to provide a protective health influence to many of the underserved who enter the criminal justice system, particularly those from violent or homeless backgrounds. However substandard, the system provides shelter and meals; it also enforces supervision and highly-structured routines, a stabilizing force for some. Moreover, several recent studies of inmate mortality rates appear to indicate that the black-white mortality gap shrinks within correctional facilities because black male inmates’ mortality rate is lower than that of the general black male population but white male inmates’ mortality rate is either the same or slightly higher than that of the general white male population (emphasis added). 

Think this through. The implication is that the population being incarerated lives under such dangerous circumstances in their communities that they suffer little or no additional risk while they are behind bars. And it's not okay if a preventable health problem is acquired in prison even if the person was equally likely to suffer outside of prison.

More importantly, as the authors note, the apparent 'benefit' of being incarcerated disappears when you include the health effects of imprisonment that manifest themselves following release. The effects of HIV, hepatitis C, other infections, or the chronic stress of confinement will affect former prisoners for their entire lives. I was stunned, however, to learn about the health risks suffered by prisoners in the days immediately following their release:

Each year, more than 600,000 inmates leave prison and more than 7 million leave jail... The weeks following release from a correctional facility present extraordinary risks to releasees and others. Former prisoners are 12 times more likely than the general public to die of any cause in the 2 weeks following release and 129 times more likely to die of a drug overdose. Some of this postrelease mortality is due to “compassionate release” of the dying, but much of it reflects the instability of circumstances in the days following release and the concomitant return to high-risk behaviors.  

Again, think this through: It is not just that we are failing to rehabilitate persons in prison, we are setting them up for immediate and catastrophic life failure.

There's lots to do to improve the health care of prisoners (my wife and I have made a small contribution here to the literature on health care for youths in juvenile justice). The most important thing we can do, however, is to find methods to control felons that avoid prolonged incarceration. Read Mark Kleiman's superb When Brute Force Fails: How to Have Less Crime and Less Punishment, for an argument about why and how to do this.

And, Canadians, think very carefully about whether you want to follow US policies of mass incarceration. 

 

The Republican Individual Mandate?

post by Paul Kelleher (@kelleher_)

Last May Timothy Jost wrote:

Fifth, both plans penalize Americans who choose to remain outside the insurance market, undermining the risk pool. The “individual mandate,” which imposes a tax penalty on Americans who can afford health insurance but choose not to buy it, is one of the most controversial provisions of the ACA. The Republican tax credit, however, is offered only to Americans who purchase health insurance. An incentive available only to those who comply with a condition is the exact economic equivalent of a penalty imposed for noncompliance with the same condition. Indeed, the price of remaining uninsured imposed by the Roadmap will in most instances be greater than that under the ACA.

In response I wrote:

I’m not sure if “exact economic equivalent” is a technical term. I assume it is. But to a lay person, this just seems wrong. In the Ryan framework, if you forgo insurance, you forgo a benefit (the tax credit). In the ACA framework, if you forgo insurance, you incur a penalty (the tax penalty). Only in the latter case are you made worse off by forgoing insurance (setting aside the benefit that insurance itself may confer).

Saying that the credit is the equivalent of a penalty seems, therefore, to be akin to saying that benefits are equivalent to harms. But that can’t be right, can it? If Congress wanted everyone to buy a state-made car, it could require them to on pain of penalty, or it could give them a discount (same as a credit) if they chose to buy. Surely these are different. If not “economically” then morally. Or am I missing something?

Fast forward to last week, when Ezra Klein wrote a column entitled "Individual mandate is Ryan Tax credit by other name." I was prepared for Klein simply to repeat Jost's claim, but Klein instead added a key dimension that Jost leaves out:

The tax credit, meanwhile, is essentially indistinguishable from the mandate. Ryan’s plan offers a $2,300 refundable tax credit to individuals and a $5,700 credit to families who buy private health insurance. Of course, tax credits aren’t free. In effect, what Ryan’s plan does is raises taxes and/or cuts services by the cost of his credit and then rebates the difference to everyone who signs up for health insurance. It’s essentially a roundabout version of the individual mandate, which directly taxes people who don’t buy health insurance in the first place. [...]

Here Klein observes that a tax credit is akin to a mandate only if uninsured individuals face the prospect of being made made worse off either by a commensurate increase in taxes or reduction in services. This background assumption has not always been made explicit by those claiming that a tax credit is the same as a mandate, but it seems to me a precondition for characterizing them as as "exact equivalents." It is still not clear to me that this was an assumption Jost was making. 

The prediction of disease from static genomic information

post by Bill Gardner (@Bill_Gardner)

One of the promises of personalized medicine has been that by sequencing our genes, we will obtain a greatly enhanced ability to characterize our disease risks. I am skeptical.

Brad Flansbaum (@BradleyFlansbaum) kindly pointed me to an article by Gina Kolata about a recent study that took a novel approach to ascertaining the likely predictive power of static genomic information. By static genomic information, I mean what can be learned by sequencing a person's genome once. The authors include the noted statistician Giovanni Parmigiani and the why-doesn't-he-have-a-nobel-yet physician Bert Vogelstein, the discoverer of the p53 gene.

The authors sought to ascertain the predictive power of genomes by looking at the long term disease outcomes of identical twins gathered from many twin registries.

Our estimates were derived from the analysis of large numbers of monozygotic twin pairs; twins of a pair share the same genometype and therefore identical genetic risk factors. Our analyses indicate that: (i) for 23 of the 24 diseases, the majority of individuals will receive negative test results, (ii) these negative test results will, in general, not be very informative, as the risk of developing 19 of the 24 diseases in those who test negative will still be, at minimum, 50 - 80% of that in the general population, and (iii) on the positive side, in the best-case scenario more than 90% of tested individuals might be alerted to a clinically significant predisposition to at least one disease.

"Clinically significant risk" does not, by the way, mean that you highly likely to get a disease. What it meant was that the risk was greater than a threshold of "a minimum 10% total risk and a minimum relative risk of 2." The authors caution us, however, that the best case scenario almost certainly overestimates the information value of a positive result.

So, as I read these results, here is what a patient can learn from the results of gene sequencing.

• If you learn that you do not have a genetic risk factor from the disease, you will know that you are somewhat less likely to get the disease than a random person. But you can still get it.
• If you learn that you do have a genetic risk factor, you are more likely to get the disease than a random person. But not that likely: most of the time, that disease will not be what kills you.

If this is how it works out, personalized medicine based on static genomic information will be only moderately useful for clinical care. So my choice is not to get myself sequenced.

That doesn't mean personalized medicine won't work some other way -- look here for a link to an article about how dynamic gene expression and other 'omic' data might revolutionize medicine.

What effective personalized medicine may look like

post by Bill Gardner

Chen and colleagues in Cell:

Personalized medicine is expected to benefit from combining genomic information with regular moni- toring of physiological states by multiple high- throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, me- tabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connect- ing genomic information with additional dynamic omics activity. 

That is, they carried out an initial genomic sequencing for a person, and then longitudinally observed gene expression and other molecular events for more than a year. Their initial findings included a genetic anomaly associated with type 2 diabetes. During the following year they tracked the molecular sequelae of the onset of type 2 diabetes. It is something like being the first observer to point a telescope at Jupiter, and discovering that it has moons.

Our study indicates that disease risk can be assessed from a genome sequence and illustrates how traits associated with disease can be monitored to identify varying physiological stages. We show that large numbers of molecular components are present in blood samples and can be measured (>3 billion measurements taken over 20 time points)... By observing dynamic molecular changes that correspond to physiological states, this proof-of-principle study offers a pilot implementation of personalized medicine.  

I've argued that by itself, gene sequencing doesn't seem to be enough to trigger a revolution in clinical care. One case proves nothing, but my intuition is that this will be a much more powerful technology.

(h/t Alex Tabarrok)