As you well know, we represent folks in delay-of-diagnosing-cancer claims. That is, a patient will have presented with signs and symptoms of cancer, but a practitioner for some reason did not work up, consider, or evaluate the symptoms, lab results, or imaging results to get the patient on track for cancer treatment. We tell clients or potential clients nearly every day that the delay must be significant enough to have made a meaningful impact on the patient’s treatment plan. What does that mean, though? We work with experts in every one of these cases to identify if, on a more probable than not basis, the patient’s current conditions, treatment, and prognosis are substantially different than if treatment had started at earlier set points in time. So, in a general sense, is there a way to quantify what the delay means to a patient’s likely outcome?
A meta-analysis of twenty years worth of studies was recently published in the British Medical Journal (the BMJ), that concluded that yes, it is possible to quantify differences in mortality by four week intervals in treatment delays.
Researchers analyzed studies from twenty years of cancer research on bladder, breast, colon, rectum, lung, head and neck, and cervical cancers across a spectrum of demographics. These seven cancers represent slightly less than half of all incidents of cancer around the world. The analysis was meant to focus on highlighting the need to minimize system level delays in treatment. It did not break out results related to patient factors, negligent delays in diagnosis, or the many variables (such as tumor types) within each type of disease.
What is the takeaway? Well, the conclusion is that a one-month delay for appropriate treatment (e.g. radiotherapy for head and neck or breast cancer or systemic treatment for colon or breast cancer) correlates measurably with increased mortality. And in a large scale, each one month delay translates to a measurable amount of excess deaths. In an immediate sense, for example, “an eight week delay in breast cancer surgery would increase [an individual’s] risk of death by 17%.”
Statistical analyses like these help to further reinforce that delays in treatment have clear adverse consequences to patient outcomes. While each case is unique, they will each share common characteristics with the cases in the study, and ideally provide support for expert testimony in delay to diagnose cancer cases.
