The Antidote

Counterspin for Health Care and Health News

Thursday, November 30, 2006

Warning: shameless self promotion

Thanks to Dr. Hsien-Hsien Lei at Genetics and Health for doing me the honor of interviewing me for her blog. She posted it today. So exciting! (And interviews are a really great idea that I will clearly have to steal one of these days.)

Something's fishy

This post is a little outside what I usually write about, but it's an area that's been important to me for years. Kim O'Donnel, who writes about food for, has been covering the environmental sustainability of fish populations, in conjunction with the health benefits and risks of eating fish, in her blog this week; she's not done, so I can't give away the story, but one message is that our current fish consumption, and in particular where it would go if we were all to follow current health recommendations (two 3-oz. portions per person per week), is not sustainable, at least if we keep gobbling up the big three, salmon, tuna, and shrimp. So Kim proposes exploring other fish: sardines, tilapia, mussels, carp, and squid, for example.

I have to say I'm frustrated at how many restaurants still serve cod, which is particularly threatened. If you go up to New England, where I'm from, folks seem to think it's their birthright to eat cod until it's gone, and the Boston Globe regularly prints cod recipes without any acknowledgment of overfishing.

Here's a 2-page PDF that folds into a wallet-sized guide to better (and worse) seafood choices.

Monday, November 27, 2006

Announcement: Grand Rounds

Heads up, everyone - I will be hosting Grand Rounds next week. Deadline is Sunday, December 3, 6 pm EST. Please submit posts for consideration to me at emily [dot] devoto [at] gmail [dot] com.

Guidelines for submitting? Yes, there are guidelines - but I am relying on the experience of the generous and erudite Dr. Anonymous; his blog offers lots of data, insights, etc. on the whole Grand Rounds process, if you're interested, not to mention Grand Rounds Volume 3 No. 9. In any case, follow Dr. Anonymous' suggestions, with the exception, of course, that you should send your posts to me, and by the Dec. 3 deadline.

Wednesday, November 22, 2006

One more for the road...

Today's New York Times reports on a new randomized trial (published in JAMA) that shows that waiting is as good as surgery in most cases (even if it doesn't provide as quick results) for sciatica (caused by ruptured disks), because - go figure - people usually recover eventually.

Interestingly, the editorialist was disappointed because the results didn't provide a clear answer as to which approach was preferable. But guess what? Patients now get to decide for themselves, instead of relying on their surgeons, who get reimbursed many thousands of dollars for a disk operation.

Weekly this and that - Nov. 22, 2006

Troops are dying; evidence needed

An off-label use of a hemophilia drug, Factor VII, for stopping bleeding is being used to treat wounded soldiers at the front lines in Iraq, according to the Associated Press, based on a Baltimore Sun article. However, the FDA has warned against its use in non-hemophiliacs, because it has been shown to lead to heart attacks and strokes. The AP article contrasts two sets of anecdotes:
"I've seen it with my own eyes," said Air Force Lt. Col. Jeffrey Bailey, a trauma surgeon deployed this summer as senior physician at the American military hospital in Balad, Iraq. "Patients who are hemorrhaging to death, they get the drug and it stops. Factor VII saves their lives."

However, doctors at military hospitals in Germany and the United States have reported unusual and sometimes fatal blood clots in soldiers evacuated from Iraq, including unexplained strokes, heart attacks and pulmonary embolisms, or blood clots in the lungs. And some have begun to suspect Factor VII, The Sun reported.
It's the representative from Factor VII manufacturer Novo Nordisk who sums up what needs to be done:
"It's really not a question of an absolute safety level, but rather a ratio of benefit to risk that has to be established," said Dr. Michael Shalmi, vice president of biopharmaceuticals for Novo Nordisk.
How are you going to do an adequately controlled randomized trial to answer this question, given the fog of war and all that? Tough problem.

Evidence helps avoid too much health care

A systematic review reported in Reuters Health finds that antibiotics usually don't help against short-term bronchitis, most of which is viral. The study cites the downsides of antibiotic overuse: cost, side effects, and antibiotic resistance.

Patient safety: guidance for hospitals

The Agency for Healthcare Research and Quality last week, at the annual patient safety meeting of the Joint Commission on Accreditation of Hospital Organizations (JCAHO), released a 10-point tip sheet for hospitals on promoting patient safety. The press release is here, and the tip sheet can be found here. It's not a simple checklist of easy things you can do like "Wash your hands" (well, maybe that's not so easy), and most of the tips will require quite a lot of retooling and work to implement, but they're all based on AHRQ's extensive research on hospital patient safety.

Expanding coverage?

The Association of Health Insurance Plans (AHIP) has announced a proposal to expand health insurance coverage to all children within 3 years and to 95% of adults within 10 years, through several strategies:
The plan would expand eligibility for public programs, enable all consumers to purchase health insurance with pre-tax dollars, provide financial assistance to help working families afford coverage, and encourage states to develop and implement access proposals.
As the Workplace Prof Blog points out, the insurance industry does have something to gain, by expanding the number of people who can buy insurance from their kids, but pointed out that the proposal (which I also found sketchy and vague) doesn't say how this plan would be paid for, and how it would take into account increasing health care costs. In any case, it's probably not ready for prime time.

Monday, November 20, 2006

What do I mean by high-quality research?

Let me start out by confusing you, just a bit. My readers have heard me talk about high-quality health research, and health care quality research, which overlaps with the former, and then, of course, there's high-quality health care quality research, which is a subset of both. Got it?

What I want to talk about today is high-quality health research, because I've alluded to it before, and because it's a key area of understanding for journalists who write about health, for health practitioners, for health policymakers, and even for plain old citizens. Learning to tell a good study from a not-so-good one isn't something typically taught in high school, though I would argue it should be. I'd also like to point out that there's a role for not-so-good studies; some of them, arguably, may not deserve to exist, but in a small way, pretty much every study that gets published could be a small stitch in the larger fabric of research, so it's of interest to other scientists in the same field.

In my book, a "good" study is one that avoids confounding - a common problem with observational (epidemiologic) studies. Let's say we're looking at the association between a predictor variable X (e.g., oat bran consumption) and an outcome variable Y (blood cholesterol). A confounder (C) is a factor that is related to both X and Y; a possible confounder in this example is milk consumption, since many people eat milk with their oatmeal, and it contains saturated fat, which might be related to cholesterol. As a reader, you want to think about whether the study authors have accounted for conceivable confounders in their analysis. You could say that epidemiologists' jobs revolve around keeping such extraneous explanatory factors out of their data. I like to think of bias as the process by which confounders are introduced into a study.

Another way to avoid bias, and hence confounding, is to randomly assign subjects to a treatment group or a control group. Using the above example, the treatment group could get oat-bran cereal and the control group could get wheat cereal; you could establish ahead of time that people used similar amounts of milk on both kinds of cereal, and also have them measure the amount of milk they used to make sure. But that's a little beside the point, which is that any other factors that might be associated with either oat-bran consumption (aspects of a healthy lifestyle, whatever that means) or with blood cholesterol are equally distributed between the two groups. If that is the case, then they can't affect the results.

Other important criteria:

* reports real health outcomes. This point probably speaks more to relevance than to actual study quality, but do pay attention to the conclusions that the authors draw, and think about how closely the reported outcome is related to the health outcome of interest. But don't eliminate an intermediate outcome from consideration just because it's not a health outcome - for example, we may actually want to know something about the delivery of diabetes care. I'll come back to this question in another post.

* generalizable. Say you're looking at a small, randomized trial of an intervention to prevent falling in a rehabilitation home for disabled veterans aged 35-50. Is the intervention equally useful for community-dwelling elderly people? The authors should address this; if they don't, you're entitled to raise an eyebrow.

* adequately controlled. This is a fundamental characteristic of scientific studies. You have to have a control group, and it's best if the controls are as similar as possible to the group with the disease or exposure of interest. The goal is to have the comparison groups differ only by the factor that you're testing. The best way to do this is, again, to randomly assign an exposure, but this is not always feasible, or ethical, in people - for example, when a factor of interest is considered to be harmful, like an environmental exposure.

* biologically plausible. It's very likely that plausibility will be addressed in the paper's introduction, and at some length in the discussion; scientists love to talk about it. However, there are some statistical types who don't get it - an association is an association, after all. To which I would answer, there is no statistical test for unmeasured confounding, so you'd better have a good understanding of how your predictor and outcome variables actually fit together.

I think these points cover the main things you should look for. There's more, but I do have to save something for another day: Adequate follow-up time. The study-design hierarchy. The role of the funders in the project. I'm happy to take requests here as well.

Wednesday, November 15, 2006

Health policy resources from KFF

Kaiser Family Foundation just announced a new resource, a monthly announcement of and links to health policy reports called Health Policy Picks. They're categorized (so far) under Medicaid/SCHIP, Health Systems, Medicare, and Minority Health.

And for those of you who like to go to meetings (or watch webcasts), you might want to bookmark KFF's calendar.

Weekly this and that - November 15, 2006

There's a lot going on in the health news realm in the past week. Here's what I hope will be a useful cross-section.

The mid-term elections

Rob Cunningham of the Health Affairs Blog provides a wrap-up on likely directions for health policy given the new Democratic majority, keeping in mind that the majority is, after all, a very slim one. Be sure to take a look at the complete Health Affairs coverage of the election, linked at the foot of the post.

Lung cancer screening: a follow-up

Sandra Boodman of the Washington Post wrote a well-balanced article following up on the recent New England Journal study by Henschke et al. (I blogged about it a few weeks ago). In this article, Boodman fully acknowledges the controversy engendered by the article and interviews smokers and radiologists about their thoughts and intentions regarding the new research. Curious, isn't it, that a radiologist in private practice thinks we should go ahead and start screening smokers en masse, even before the definitive study comes out? Or am I just too cynical?

Red meat and breast cancer

My colleagues at the Knight Science Journalism Tracker and the Health Behavior Blog kindly summed up some of the news coverage of the new finding from the Nurses' Health Study II, that women who ate red meat regularly in their 20s through 40s were at higher risk of breast cancer. I was probably too busy feeling smug about having all but given up beef in my 20s to get exercised about yet another single observational risk-factor epidemiology study.

Disparities redux

Reuters reported this week on a study at the eminent Duke University Hospital in Durham, NC, home to a proud history of psychosocial research. The study showed that black men treated for heart disease at the hospital had lower mortality than white men treated there, even though their younger average age should have made up for lower scores on other factors that aggravate heart disease. This study was apparently reported at a meeting, and I am loath to report on items where I can't look at the data, but with luck I'll get a chance to see this one in print soon. In any case, it's illustrative of an important aspect of health disparities: possible effects on health outcomes from discrimination on the part of providers. Again, no details available; I include it merely as food for thought.

And this just in from the same meeting: a study showing that whites receive cardiopulmonary resuscitation for heart attacks more often than blacks, possibly because blacks are less likely to get CPR training than whites. A little more straightforward than the previous example, perhaps.

Sin taxes and the AMA

The AP reported yesterday that the American Medical Association declined to endorse a proposal to tax soft drinks. Apparently they were uncomfortable with a Federal tax, but it's unclear why. Taxing cigarettes is the most effective way to prevent smoking, so it stands to reason that taxing soft drinks would work the same way to help prevent obesity. But wait a second - we know that preventing smoking protects health, but how good is the evidence that preventing soft drink consumption prevents obesity or, more importantly, actual health endpoints such as diabetes? That may be where the AMA's resolve weakened. Still, I'd like to have been in the room when the issue was debated.

Sunday, November 05, 2006

Thinking about health disparities

A number of new research articles and news stories recently have spurred me to think about how we think about the disparities in health, and what this vague term means. According to Wikipedia, the U.S. Health and Resources Services Administration (HRSA) defines it health disparities as "population-specific differences in the presence of disease, health outcomes, or access to health care." These populations are usually defined by race, ethnicity, age, sex, insurance status, rural vs. urban residence, and/or socioeconomic group.

I think of health disparities as an element of quality, or the lack thereof. Sure, everyone may be getting the same (poor-quality) health care. It's more likely that poor quality health care is seen more often in some populations than others, via reduced access to quality care (which could take many forms, from underinsurance to language barriers) or via discrimination (which can also affect health in ways unrelated to health care per se). Often, when you look at health outcomes, it's hard to tease apart the relative roles of access, discrimination, and other factors. Because access to quality care for everyone is (or should be) a priority, however, much good research is emerging on differences in care between different populations. But it isn't easy, as this post by my blogging colleague Cervantes points out, starting with the difficulties of defining ethnic populations.

Here are a few recent articles, which I chose because they illustrate different levels at which disparities are manifested:

Mays and colleagues review research on the psychological/physical effects of discrimination on health outcomes. A press release on this article explains the general mechanism of effect of discrimination thus:
When a person experiences discrimination, the body develops a cognitive response in which it recognizes the discrimination as something that is bad and should be defended against, Mays said. She said this response occurs for the most part even if the person merely perceives that discrimination is a possibility.

Starting with the brain's recognition of discrimination, the body sets into motion a series of physiological responses to protect itself from these stressful negative experiences, Mays said. These physiological responses include biochemical reactions, hyper-vigilance and elevated blood pressure and heart rate. With many African Americans, these responses may occur so frequently that they eventually result in the physiological system not working correctly.
A second paper, by Trivedi et al. documents lower-quality care received by older African-Americans compared to whites. Specifically, they are less likely than whites to have their blood pressure, cholesterol, and blood sugar under control. Each of these measures is a reliable indicator of health-care quality. The results were not explained by blacks being in lower-quality health plans; the differences were seen within all 115 of the Medicare plans studied. The paper did not settle the question of why such differences exist, but it did find that demographic factors like income and education explained only some of the gap observed and, of course, lifestyle factors like diet that do not relate to quality likely explain some of the results.

I would suggest that a next step would be to tease apart the contributions of lifestyle and health care quality to the health differences - one way to do that would be to compare process measures, which measure actual delivery of care as opposed to health outcomes. I checked the National Healthcare Disparities Report; the 2005 report gives a similar result to the Trivedi paper - control of hemoglobin A1C (a measure of blood glucose) is better in whites than blacks. However, 2004 report presents a related process measure: adults with diabetes who had a hemoglobin A1C measurement at least once in the past year. Interestingly, for this measure, blacks and whites appear to be approximately equivalent. This is not to say that A1C measurement is not related to good diabetes outcomes, or in other words unrelated to quality, but it demonstrates the role of other factors - possibly even care-related factors - in determining health outcomes. (By the way, I highly recommend the above-cited Disparities Report, and its companion the National Healthcare Quality Report, as useful overviews of U.S. data on healthcare quality; I did play an advisory role in both of these documents.)

In an accompanying press release to the Trivedi study, the first author noted that many plans don't even collect information on race and ethnicity of their patients, so they may not even know they have a problem. Perhaps this can be explained by a naive and unfortunate assumption that their care is race-blind, but it certainly means that any existing disparities will go unaddressed.

A third paper on disparities relates somewhat, but less directly, to quality. A little background: in the past few years, studies have emerged that address the hypothesis of whether the number of surgeries of a particular type (i.e., surgical volume) done by individual surgeons or within a facility is related to health outcomes. The consensus seems to be that volume at the facility level, but not at the surgeon level, is related to outcomes. In other words, facility-level surgical volume is an indicator of quality. The paper by Liu et al. found that, minorities c minorities (Blacks, Asians, Hispanics) compared to white patients, the uninsured, and Medicaid patients were more likely to receive surgical care at lower-volume surgical centers. That's not a direct measure of either discrimination or access to care related to race or ethnicity, but could represent geographic differences (e.g., proximity to quality hospitals), and the fact that it's related to economic differences (Medicaid and uninsured populations) suggests that access to care could play a role. An editorialist on the study pointed out that referral of patients to higher-volume hospitals does not solve the problem of quality differences, but is an "end run" around it, by shifting patients away.

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