CHA Family Medicine Residency

Tufts family medicine residents have "the best of both worlds"

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UberDx Chapter 6

Harding PPThe latest salvo in the mammography overdiagnosis debate, from Harding et al at

JAMA Intern Med. Published online July 06, 2015. doi:10.1001/jamainternmed.2015.3043

A classic overdiagnosis curve, showing a dramatic increase in breast cancer diagnosis, as related to proportion of women getting mammograms, without a commensurate change in 10 year mortality.

If we can’t stop the mammography juggernaut, we should at least inform our patients of the risks of overdiagnosis.


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UberDiagnosis – Chapter 5: the NNT and NNH

You’ve heard about the “Number Needed to Treat” and the “Number Needed to Harm.” This statistic guesstimates how many people we need to screen in the clinic/hospital before we find a benefit or harm from a treatment.

Has your family or friends ever heard of the NNT? Have your patients ever asked: “what’s the NNH for this?


Well, this is an exciting year for biostatistics because NNT and NNH have hit pop culture and the mainstream media in a big way!


First, it started in October 2014 in the Wired Magazine. Then, PBS spread the word in December 2014.
Next, it hit the NY Times two months ago:How to Measure a Medical Treatment’s Potential for Harm


Then the AFP decided to get in on the action in this month’s AAFP: Introducing Medicine by the Numbers: A Collaboration of The NNT Group and AFP

This new series will appear in the online-only edition of AFP. Each month, medical editors from AFP and will select a topic to feature. We will use’s color-coding to quickly convey the relative merits of an intervention, and present the numbers for benefit and harm in a summary box. A discussion outlines the background evidence and the rationale for the rating, accompanied by key supporting references.



If you’ve worked with me, you might have heard me quoting things like “1 out of 8 to 9 people who are treated for AOM or sinusitis with antibiotics with GI upset and diarrhea.”

Where did I get these facts? How do I know them off the top of my head?

From the great website! In addition to facts on treatment, they also have launched a new section on diagnosis (and most relevantly here, on harms associated with overdiagnosis.)


Here are some of the relevant links to check out below. How often have you asked yourself a question about these clinical issues in the past month?

Primary Care: Diagnosis


Primary Care: Treatment


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UberDx Chapter 4

So now you know about Overdiagnosis Bias. The answer to Question 1 in Chapter 3 is “False”. In this scenario, screening does not diagnose more cancers, but the numerator and denominator are inflated because of all the false positives and “pseudodisease” screening creates. So it looks like the 10 year survival is better. Have you had to deal with this recently, as CHA has embarked on screening smokers for lung cancer? I certainly have. I really try to engage patients in shared decision-making about that and I basically try to talk them out of it. We know how to prevent lung cancer.

Ok, so take a look at this:

Cancer incidence


Can you think if some cancers that fit the A graph: we pick up more aggressive cancers by screening? How about the B graph: we seem to diagnose more, but there is no corresponding increase in number of people dying from the diagnosis? In B we are: 1. Picking up more benign cancers, 2. Simultaneously improving treatment while picking up more cancers, 3. Diagnosing more, maybe earlier, but having no effect on survival.

There aren’t too many good examples of “A”, at least for cancer. Alzheimer’s disease fits the graph pretty well, though. Cervical cancer is actually a good example, but the death rate begins to fall off after time, because we have good interventions.

There are a lot of examples that fit “B”. Prostate cancer. Thyroid cancer. Can you think of any others?

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UberDx Chapter 3

Did you read (See emails below)

If not, proceed no further. Even if you think you know everything, you still have to read it.

If you have, then this graphic should look familiar:

Overdiagnosis bias

Overdiagnosis Bias: The over-inflation of survival statistic by “early diagnosis”.

So, now think about these questions:

  1. More lung cancers are detected in screened populations than in unscreened populations. True or false?
  2. What are some examples of “pseudodisease” in the case of lung cancer screening? (“the small solitary pulmonary nodule”)
  3. What reactions have your patients had from a CT scan that requires follow up to rule out cancer?

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UberDx! 2

By the way, we appreciate all comments. It’s hard to know whether this stuff is just getting deleted, or scoffed at, yawned at, laughed at, marveled at or what.

Hopefully you read the wiki at
If not, please go for it. It’s short. Try to get an idea of Overdiagnosis Bias.
We had planned a second informative message for this week, but then this YouTube showed up. You gotta see this:


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UberDx – Introduction

Welcome to “UberDx”!

Kind of a snappy title, right? If you find it offensive, please let us know.

It is about Overdiagnosis. So think “uber” as in “over”, but really it’s about “better” as well, because we all want to make better diagnoses and avoid making bad ones. We are focusing on the bad diagnoses – the ones that hurt, mislead, mislabel – “the diagnosis of disease that will never cause symptoms or death during a patient’s lifetime”.

We are hoping that you follow this with delight. Go ahead and start a conversation!

To get started, please read this:


And if you want to delve a bit further, read this:

The challenge of overdiagnosis begins with its definition. BMJ 2015; 350 doi: (Published 04 March 2015)

Our plan is to engage you something like weekly.

  • George and Clinton