A recent article, Why the Annual Mammogram Matters, published in the NY times has reignited a passionate debate. Statements by two breast radiologists and one breast surgeon objecting to a more risk aligned screening algorithm do little to convince that the primary concerns are patient health and perhaps not more nuanced around shifting paradigms that will most certainly influence bottom lines.
I say this cautiously but with confidence that the spurious data and findings cited for defense of business as usual are flawed and limited.
What I have included here are opinions from thought leaders and physicians that have published quite extensively about extensive screening and the contribution to overdiagnosis and harms reslting from too wide of a diagnostic net without proper informed shared-decision making.
The audio clip below, Medical Students Crunch Big Data to Spot Health Trends, from National Public Radio acknowledges the need for better numeracy and data competency in medicine. Many professionals are aware that statistical signficance doesn't necessarily correlate with clinical signficance but few are aware how to interpret data at the patient level--or rationalize how findings should impact care.
A few simple truths...
For screening to prevent people from dying early, simply finding cancers is not enough; we need to find progressive cancers that would kill if left untreated. What’s more, we need effective treatment for these cancers. And the therapy has to be more likely to cure if administered earlier (when cancer may be detected by screening) than later (when cancer may be detected by the patient or doctor without screening).--When Talking about Cancer Screening, Survival Rates Mislead
Length bias also impacts the effect of screening programs on overall cancer survival. Length bias identifies indolent or slowly growing tumors that may have a longer pre-symptomatic screen-detectable timeline. Aggressive tumors don't spend much time in the slow growing period therefore detection isn't advanced by routine screening. This contributes to the often misinterpreted "perceived" survival advantage to screen-detected cases. Length bias as described in the graphic may also lead to overdiagnosis--identification of cancers that would not have impacted patient had screening not taken place.
Death rates are improved only where screening has led to a real benefit; they are unchanged where screening has no effect on natural disease progression.
Survival statistics, even when they are used by well-meaning advocates who misinterpret them as a measure of the success of cancer screening, are misleading. They tell us nothing about lives saved and the potential value of screening programs.
Here’s the bigger problem: screening mammography has failed to reduce the incidence of metastatic disease and it’s created an epidemic of a precancer called DCIS. The premise of screening is that it can find cancers destined to metastasize when they’re at an early stage so that they can be treated before they turn deadly. If this were the case, then finding and treating cancers at an early stage should reduce the rate at which cancers present at a later, metastatic stage. Unfortunately, that’s not what’s happened.--Like Groundhog Day The Mammogram Story that Won't Die
Either mammography isn't sensitive enough to identify these cancers early or they don't fit the Halstedian paradigm of steady progression. The lack of change in the incidence of metastatic disease is consistent with the hypothesis that breast cancer is a systemic disease by the time it's detectable — a paradigm typically attributed to Bernard Fisher.--Trends in Metastatic Breast and Prostate Cancer — Lessons in Cancer DynamicsH. Gilbert Welch, M.D., M.P.H., David H. Gorski, M.D., Ph.D., and Peter C. Albertsen, M.D.
More to come...Improving Numeracy in Medicine (pre-order for only $5.99--price jumps upon publication). Join discussions about how to translate population level risk to individual risk at the patient level (absolute risk). Hint: the inverse of absolute risk is number needed to treat.
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