The Surprising Effect of Mammogram Recommendations (Amy Finkelstein, MIT)

One in eight women will be diagnosed with breast cancer. The current recommendation is that women should receive annual mammograms starting at age 40. But who is actually following this recommendation, and does that affect the test’s efficacy? MIT’s Amy Finkelstein and two of her coauthors, Tamar Oostrom and Abigail Ostriker, explore this question in this video.

This video is based on the following paper:

Screening and Selection: The Case of Mammograms
Liran Einav, Amy Finkelstein, Tamar Oostrom, Abigail Ostriker, and Heidi Williams https://economics.mit.edu/files/20062

More of Amy Finkelstein’s work: https://economics.mit.edu/faculty/afink/publications

More of Tamar Oostrom’s work: https://economics.mit.edu/grad/oostrom/research

More of Abigail Ostriker’s work: https://firststreet.org/team/abigail-ostriker/

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- [Amy] What are the costs
and benefits of mammograms?

How do you weigh, for example,

the potential savings
and healthcare cost

against the potential lives saved,

against the increase
in psychic costs --

these are all very,
very difficult issues.

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My collaborators and I
have recently been looking

into the highly controversial area

of recommendations
for whether and when

to screen for breast cancer
using mammograms.

There's concerns
that not enough women

are getting screened
for mammograms.

- [Tamar] This is a hugely
relevant issue for many women.

One in eight women
get a breast cancer diagnosis

in their lifetimes,

so there's this recommendation
to get annual testings at age 40.

- There's also concerns that
many women are getting screened

and having false positives...

- [Abigail] ...which is when
you detect a tumor,

and you treat it,

but if you had left it alone,
it wouldn't have been a problem.

Overdiagnosis is a problem

because you're incurring
a lot of costs

that you really
shouldn't have had to.

It causes a lot
of anxiety to people

if they're diagnosed with cancer,

so we really want
to reduce overdiagnosis.

- So we're asking how does a person
who responds to a recommendation --

so, in this case, for mammograms --

differ from people
that don't get screened

or from the average person
in the population.

- We got data on people
who were screened,

so we could see the rates
of screening by age.

- [Abigail] Before age 40,

about 10% of people
were getting screened.

After age 40, that jumped up
to about 35% of people.

- The share of people
getting mammograms

went up drastically at 40,

but the share of people
who got mammograms

who tested positive
for cancer was going down.

But that just tells us

how the people who respond
to the recommendation at 41

differ from the people
who get mammograms

without a recommendation at 39.

It doesn't tell us
what we really wanted to know,

which is, how do the women
who are getting mammograms at 41 --

when it's recommended --

differ from the women
who aren't getting mammograms?

That's a very hard problem
to answer

because what you need is data
on the underlying cancer

of people who, by definition,
aren't being screened.

That's where the biologists
and the clinicians came in --

they developed models

of the underlying incidence
of breast cancer

in, say, a random 25-year-old
in the population

as well as, most importantly,
for our purposes,

how it progresses
in the absence of treatment.

- This is, I think, a wonderful case

where the medical community
has so much to add here,

and then the economics
community can take that

and build onto that.

- This model gave us
the underlying level of cancer,

and, using that, we could back out
what the cancer level was

for people who never got screened.

It's like supposing
you have a roomful of people.

There's ten people, and you know
that half of them have cancer,

but you don't know who.

So you pick five,
and you screen them.

and only one of them has cancer --

then you know that,
of the other five,

four of them have cancer,
even though you didn't screen them.

So we find that the people
who follow the recommendation

actually are healthier than
the people who don't follow it --

they're less likely to have cancer,

and if they're diagnosed
with cancer,

they're more likely to have
an earlier stage cancer

or a smaller cancer
that's less dangerous.

We wanted to study what types
of women get mammograms

to see whether they're the types
of women who would benefit most.

Right now, it seems like
the recommendations

are targeting people
who are most healthy.

Maybe they engage in other
preventive health behaviors,

and maybe they're doing really well,

but we're not reaching the people
who have a higher burden of cancer.

So we'd like to look into ways

that we could target
those people better.

- So, in the end, to be clear,

we don't resolve
or even attempt to resolve

the question that, in some sense,
motivates the whole literature,

which is, should we recommend
screening at 40, 45, 50, 35?

Instead, all we do is add
another piece to the puzzle.

- So our paper brings
an additional dimension

that should be considered

when sort of designing
these policies.

- You have to worry about

who the people are
that you're reaching,

and if they're the people
who are more or less at risk

for having cancer

than a randomly chosen person
in the population.

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