Stable Remission Rates Found in Insured Patients With Ovarian Cancer Amid COVID-19 Pandemic

In part 2 of this interview with Chun Chao, PhD, MS, cancer epidemiologist at Kaiser Permanente Southern California, she tells The American Journal of Managed Care® about the key findings and limitations of her study, "Clinical Remission Rates in Patients With Epithelial Ovarian Cancer Before and After the Onset of the COVID-19 Pandemic in an Integrated Healthcare Delivery System."

Watch part 1 for insights into the study's background and objectives.

This transcript has been lightly edited for clarity; captions were auto-generated.

Transcript

Your study found no significant differences in complete or clinical remission rates before and after the COVID-19 pandemic. What do you think contributed to these stable outcomes despite disruptions in care?

I think there are a few possible explanations for our findings. Similar to other studies, we found a greater use of neoadjuvant chemotherapy in ovarian cancer management in the pandemic era. The observation that we have similar remission rates before and after the onset of the pandemic may suggest that the use of neoadjuvant chemotherapy followed by surgery was a similarly effective management approach as surgery first and then adjuvant chemotherapy, so that's one possible reason.

Also, as part of the study, we analyzed data on health care utilization among patients with ovarian cancer during the active treatment phase, and we found comparable rates of physician visits. There was increased telehealth use, but, in terms of the number of visits, whether it's telehealth or in-person, that frequency is actually comparable before and after the onset of the pandemic in the Kaiser Permanente Southern California setting. We also found comparable rates of the laboratory tests that were done to monitor chemotherapy toxicity.

Another possible reason is that these patients are actually still receiving comparable care during their active treatment phase, similar contact with their physicians, and about the same monitoring for their chemotherapy. That could also contribute to the stable outcome that we observed.

Another possible factor, which we didn't directly evaluate in our study, but we did not actually find evidence for delays in treatment. We did not have evidence for a delay between the time to diagnosis and treatment initiation in our health care system. That could also be part of the reason why we saw comparable outcomes of remission for these patients.

What were the key limitations of your study? How might they have impacted your findings?

There is always a possibility of unmeasured confounders that we did not account for in our study. Also, not all our patients had follow-up visits that would allow us to assess their remission status.

However, we did have very detailed information on, I would say, the most important prognostic factors. The one thing we did not have was the molecular characteristics of these ovarian cancers, but that's one thing that we did not expect to vary, the distribution of the molecular characteristics of ovarian cancer. We did not expect that to vary before and after the onset of the pandemic, so that should not be a huge concern.

In summary, I don't think we actually expect these limitations to have an important impact on our findings.

Since your study focused on an insured population, how do you think remission rates might have differed for uninsured patients?

We know that cancer outcomes, including ovarian cancer, are generally worse in the uninsured patient populations, and there are several reasons for this. There could be a delay in care seeking, a delay or suboptimal treatment because of cost concerns, and more limited access to specialists. They also may have less optimal adherence to recommended treatments for these reasons.

For these reasons, our findings may not be generalizable to these other populations, including the uninsured or different types of health care settings. We found the remission rate to be stable before and after the onset of the pandemic. But, in uninsured patients, for example, they may have even greater barriers in care seeking in the pandemic era, so there may be more loss of employment and, also, a greater or new childcare need for these patients during the pandemic. All these factors will impose greater barriers for them to seek care and receive adequate treatment.

That's why the findings in an insured population may not apply to the uninsured population when you compare the pre- and post-pandemic eras because people experience different challenges or barriers.