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Blog
Blog Series: Payer Coverage Changes in the Time of COVID-19 – What to Track to Minimize the Impact on Manufacturers
Two Commercial Questions Manufacturers Must Answer Amid Payer Coverage Changes
Sep 22, 2020
The massive loss of employment in the U.S. represents a huge disruption to the health insurance market and has broadly impacted pharmaceutical manufacturers. The natural response, from a commercial perspective, is to seek answers to two key questions:
- How do I track what is happening to patients losing employer-sponsored coverage?
- How do I change my go-to-market approach based on this information?
While a lot of discussion has been devoted to how best to react to patients losing coverage, it is also important to understand the methodological considerations of measuring the impact. This will help to ensure that the implications and biases in different measurement approaches are well understood.
Prescriptions vs Lives
The two main “units” that can be used to track the relative share of different coverage options over time are prescriptions and covered lives. Data for both are readily available, if not public, and give an indication of the size of different insurance pools. Lives is obviously a direct measure of plan enrollment, but many analyses based on covered lives often neglect to include details around inflows and outflows, preferring to consider net patient enrollment.
As an example, recent reports of Medicaid enrollment growth would lead one to believe that many uninsured patients are seeking out coverage via Medicaid. But deeper analysis reveals that Medicaid has simply suspended programs aimed at moving people off Medicaid who no longer qualify. This growth in Medicaid is attributed to fewer people leaving rather than more people joining – and this changes the conclusion.
Compared to Lives, TRxs are generally a more consistent unit of measure for tracking these analyses, as they represent true patient utilization. On top of this, they also allow for more precise geographic and therapy-specific analysis since each observation is an actual prescription with detailed information. The downside of TRxs as a unit of measure is that they can be heavily biased towards patients who are taking more medications.
As has been widely reported, unemployment trends have been harsher on younger Americans who tend to use less healthcare1. Because of this, we might expect TRx-based approaches to show a relatively muted impact of the loss of employer-sponsored coverage. Yet, looked at another way, this muted impact is, in fact, what manufacturers should be considering since patients who are not utilizing pharmaceuticals will not impact a manufacturer’s strategy.
There is actually a third perspective to consider when tracking pandemic-influences insurance coverage trends: patients. Patients are distinct from Lives in that when measuring impact, we only count those patients whom we observe to have claim activity -- similar to TRxs -- but we count each patient only once. This reduces any biases in TRx-based approaches, while avoiding the issues inherent with Lives-based ones.
Point-in-Time vs Longitudinal
The second major consideration for tracking COVID-19 impact on patient coverage is how time is taken into account. The simplest approach is to make a series of observations and use the findings to form a trend line.
For example, one might look at the share of TRxs using a Commercial payer over time. Doing so yields a surprisingly low amount of change over time, given what we know about the loss of coverage. This methodology, while still accurate, is simply not sensitive enough to the movement of a few million patients. The patients who are experiencing disruption of coverage are included in this data, but their experience is being obfuscated by the much larger group of patients who have not experienced a change in coverage.
Conversely, one could take an approach based on tracking a cohort of patients over time. This approach is advantageous because it enables unambiguous identification and tracking of an “impacted” population without requiring that this population be especially large relative to the entire study pool. This allows for deeper investigations without losing visibility into the scope of impact.
IQVIA’s COVID tracking tool uses a patient-based based approach. While not the end-all and be-all analysis for assessing patient coverage changes, it is the most robust standalone approach that also allows for triangulation of insights vis-à-vis other analytical methods.
This resource is Part 2 of a blog series dedicated to Payer Coverage changes in the time of COVID-19. Our next post will review the three payer switch scenarios, what to consider, and how to adjust strategy.
For more guidance on how to track payer coverage, contact Brian Fallica.
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