{"id":828,"date":"2009-11-19T15:51:28","date_gmt":"2009-11-19T15:51:28","guid":{"rendered":"http:\/\/scientopia.org\/blogs\/goodmath\/2009\/11\/19\/the-balance-of-screening-tests\/"},"modified":"2009-11-19T15:51:28","modified_gmt":"2009-11-19T15:51:28","slug":"the-balance-of-screening-tests","status":"publish","type":"post","link":"http:\/\/www.goodmath.org\/blog\/2009\/11\/19\/the-balance-of-screening-tests\/","title":{"rendered":"The Balance of Screening Tests"},"content":{"rendered":"

As you’ve no doubt heard by now, there’s been a new recommendation issues
\nwhich proposes changing the breast-cancer screening protocol for women under
\n50, by eliminating mammograms for women who don’t have significant risk
\nfactos. While Orac has done a terrific job of covering this here<\/a> and
\nhere<\/a>, I wanted to throw
\nin a couple of notes and a personal perspective.<\/p>\n

<\/p>\n

To begin with, there’s a bit of math which has been bandied about, and
\nI thought I’d just quickly walk through it.<\/p>\n

When you look at things like screening programs, what you’re doing is
\nperforming some kind of test on a very large population, in the hopes of
\nfinding a comparatively small number of serious illnesses. Any process
\nlike that is, necessarily a tradeoff.<\/p>\n

I’m going to use totally fake<\/em> numbers to explain this – so don’t
\nthink that these numbers are real.<\/p>\n

Suppose that you’ve got a non-contagious disease which will be caught by
\none out of every 1,000 individuals. That’s a fairly rare disease. If
\nit were harmless, you’d completely ignore it – you wouldn’t even bother
\nspending money on researching a cure for it. It’s just not worth the
\ntrouble.<\/p>\n

Now, suppose that the disease is universally fatal. Only one person out of
\nevery 1,000 will catch it – but all<\/em> of them will die. In this case,
\nthere’s not much point in trying to figure out who has it – there’s nothing
\nyou can do for them. But you would start spending money to figure out how
\nto cure it.<\/p>\n

Now for the tricky case. Suppose that the disease isn’t universally
\nfatal. It’s almost always fatal if it’s had time to establish itself. But
\nif you catch it early enough, then you can with high probability, save the
\nperson who has it. This is the case for cancers where we consider
\ndoing screening.<\/p>\n

In this last case, there’s a very complicated tradeoff. Should you
\nspend time and money trying to detect the disease? Or should you spend
\nthe time and money trying to discover better treatments for the disease?\n<\/p>\n

In fact, the tradeoff is ever worse that that, for two reasons. First, the
\nscreening process has a huge false positive rate. The screening process comes
\nup positive in 5 out of every 1000 cases. So you wind up treating 4 people who
\ndon’t have the disease<\/em>. And the treatment isn’t always completely
\nbenign. Most of the time, you do an additional test, and that’s it. But that
\nadditional test has some amount of risk to it – some people will end up dying
\nas a result of the treatment for a disease that they didn’t have. And second,
\nthe test itself isn’t risk free: it’s got a small but real chance of
\ncausing exactly the disease that it’s being used to detect!<\/p>\n

So how do you set a balance? You can screen people – and save some number
\nof lives by detecting disease that would have killed them had it gone
\nundetected. But by doing that, you’ll give the disease to some number of
\npeople who wouldn’t have gotten it otherwise; and you’ll harm some number of
\npeople who were caught in the screening process, but didn’t have the disease
\nat all.<\/p>\n

It ends up coming down to a mathematical optimization process. You want to
\nmaximize the survival rate of the population as a whole. You do that by
\nputting together a lot of factors: how many people will get the disease? How
\nmany will miss detection if you don’t do the screening? How many will get the
\ndisease as a result of the screening? How many will be harmed by procedures
\ndone as a result of false positives? And how many could have been saved by
\nspending money on developing cures instead of screening?<\/p>\n

The current situation appears<\/em> to be that for women under
\n50 who don’t have other risk factors, it doesn’t make sense for them
\nto get the screening. The risks and costs of the screening outweigh any
\nbenefits of it.<\/p>\n

To shift to the personal side for a moment, I’d like to provide you
\nwith a concrete example of how a false positive can do harm.<\/p>\n

Close to 20 years ago now, my father had a muscular cancer in his leg. He
\nhad surgery followed by radiation to have it removed. Everything went
\nbeautifully. It turned out to be a highly aggressive cancer, but the surgery
\nappeared to have gotten it all. But because it was so aggressive, they wanted
\nto keep screening, looking for any trace of a recurrence. About two years
\nafter the surgery, they saw something<\/em> on an x-ray – it was either a
\npatch of scar tissue right by a vein, or it was the beginning of a new tumor.
\nThey did numerous tests, but nothing was able to determine definitively what
\nthe hell it was. A radiologist from Memorial Sloan-Kettering thought it was
\njust scar tissue, and recommended waiting. But the radiologist from the
\nhospital where the surgeon worked was equally sure that it was cancer. So they
\ndecided to do surgery – it was an aggressive cancer with a very low survival
\nrate; why take a chance on letting it spread? So they went in and removed it.
\nThe second surgery went very badly. It took over a year for the surgical wound
\nto heal, and there was enough circulation lost during the surgery to kill the
\nnerves in his leg. After that surgery, he could never feel or move that leg
\nbeneath the knee. For the next 18 years, it caused constant trouble with poor
\ncirculation. A blood clot in the vein affected by the surgery is what
\neventually led to his death.<\/p>\n

The point of that isn’t to tell you a sad story – but to illustrate the
\nvery real risks of any intervention. The initial surgery – the one for the
\nconfirmed cancer, was a complex procedure, due to the location and type of the
\ntumor. But the second one was quite routine – removing a one-centimeter,
\nwell-isolated mass from the muscle below the knee. It wasn’t simple, but it
\nwasn’t by any means a particularly complicated surgery either: it was the sort
\nof procedure that the surgeon geon did, on average, twice a week! But even a
\nroutine procedure has risks. Even the most routine procedure can, in rare
\ncases, wind up killing you.<\/p>\n

That’s the point of the optimization problem that’s used to figure out
\nwhether or not to screen for a potentially deadly disease: no intervention is
\never free – and I don’t mean that just in terms of money. Every intervention
\ncomes with an associated risk. You have to find out where the balance point is
\nbetween the risks that you’re trying to protect people from, and the risks
\nthat you’re going to inflict on people in the process of protecting them.<\/p>\n

The best recent evidence, when put into that optimization problem, has
\nstrongly suggested that the mammograms in women under 50 aren’t worth the
\nrisk. The risk of the radiation of a yearly mammogram plus the risks of the
\nbiopsies end up being worse, on average, than not doing the mammogram. The
\nbalance point between risk and benefit doesn’t work out well until you
\nshift the pool of people being screened to be older – and thus at higher
\nrisk.<\/p>\n

The natural response to this is to say: but what about the women under 50
\nwho have cancer that would have been detected early enough to be cured? Isn’t
\nrefusing to give them mammograms harming them?<\/p>\n

Yes, it is. But giving<\/em> the mammograms will harm a different
\ngroup of women – and it appears that the group harmed by the earlier
\nmammograms is larger that the group helped by them. <\/p>\n

To give a very different example of screening balance decision, one
\nwhich again has some personal resonance for me:<\/p>\n

Most men will, at some point in their lives, suffer from gastric reflux.
\nOne of the side-effects of severe reflux is esophageal cancer – which is
\nalmost universally fatal.<\/p>\n

Esophageal cancer is actually pretty easy to screen for. The
\nvast majority of cases start as pre-cancerous lesions in the esophagus
\nlong before the cancer develops. Those lesions can easily<\/em>
\nbe detected by doing an upper endoscopy. Nearly every case of
\nreflux-driven esophageal cancer is preventable<\/em> by
\nendoscopic screening.<\/p>\n

So – if we can save all of those people, why don’t we give every man over
\n40 a yearly endoscopy? Because the endoscopy has risks. They’re not
\nparticularly common – but when they happen, they’re pretty severe (perforation
\nof the esophagus and\/or stomach, infection). Even though those occur
\nvery<\/em> rarely, they do<\/em> occur. And the risk of those injuries
\ncaused by the procedure outweigh the benefits of the procedure for men with no
\nsymptoms or risk factors for severe reflux-related disease. (The personal
\nconnection here is that I’ve had 6<\/em> endoscopies, and surgery to try to
\neliminate the reflux. I’ve also had several relatives die of esophageal cancer
\ncaused by reflux.)<\/p>\n

In contrast, we do routinely do colonoscopies to screen for colon cancer.
\nIt’s another common, dangerous cancer. And the risks of doing a colonoscopy
\nare comparable (if not a bit greater!) to the risks of an endoscopy. But it’s
\ngot no symptoms that we can use to identify people who are likely to develop
\nit. So we can’t do what we do with endoscopies – which is to select people at
\nrisk based on symptoms. So we routinely screen people when they get to the
\nright age – because the number of lives saved is less than the number of lives
\nlost due to complications.<\/p>\n

If we started doing colonoscopies at 30 instead of the current recommendation
\nof 50, we’d save some people from dying of colon cancer. But we’d also hurt
\na whole lot of people without colon cancer. So we don’t do it.<\/p>\n

It all comes back to the optimization problem: find the optimal point
\nwhere the benefits, costs, and risks balance out.<\/p>\n","protected":false},"excerpt":{"rendered":"

As you’ve no doubt heard by now, there’s been a new recommendation issues which proposes changing the breast-cancer screening protocol for women under 50, by eliminating mammograms for women who don’t have significant risk factos. While Orac has done a terrific job of covering this here and here, I wanted to throw in a couple […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[24],"tags":[],"class_list":["post-828","post","type-post","status-publish","format-standard","hentry","category-goodmath"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-dm","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/828","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=828"}],"version-history":[{"count":0,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/828\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=828"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}