Something interesting happened this month as we examined the 2018 experience of two different plan sponsors in two separate geographies with different demographics. The only similarity between the plans is they both have 80,000 or more individual claimants on their drug plan annually.

At the same time last year, we completed a series of predictive analytics on each of these plans. In the first case, we predicted it would see a one per cent decrease in plan spending. That’s not common for a group that’s been growing in terms of plan member population year over year, so it was likely met with some skepticism. As it turns out, plan costs decreased by 3.1 per cent year over year. In the second case, we predicted a 3.7 per cent increase in spending. The figure came in at 4.2 per cent. 

As we begin 2019, it’s surprising how many administrative-services only plans are still forecasting plan spending using historical numbers and/or well-established actuarial formulas that don’t leverage all the information current plan experience is telling us.

Read: Why drug plan sponsors need more complete information

In health benefits, like drug plans, the past is a poor indicator of the future. A group’s demographic and disease state severity profile changes, but more importantly, the cost drivers change significantly. This must be accounted for, as does the geographical distribution of the plan — given the significant coordination of benefits differences existing region to region — and the plan’s current design.

There’s a good reason why any industry stakeholder tasked with predicting plan or block spending in the next year missed accounting for hepatitis C claims — it’s hard to see what’s ahead if you’re only looking in the rearview mirror. In 2013, hepatitis therapies accounted for 0.6 per cent of plan spending for active plans and 0.2 per cent for retiree plans. By 2015, those figures were 3.3 per cent and 1.9 per cent respectively.

A plan may be looking to predict their claims’ experience for the end of 2019, 2020 and/or 2021 for a number of reasons, such as assisting in strategic planning around plan design, trying to value its post-employment liabilities or looking at its current standing relative to its future obligations.

Read: Drug Plan Trends Report: A snapshot of what’s coming down the pike

Here are some elements to consider when examining the existing experience:

  • How many new chronic specialty drug claimants will emerge in the next 12, 24 and 36 months and in which disease-state areas? That can be assessed by examining the group’s current disease state severity profile.
  • What will be the impact of the near-term drug pipeline on the plan? One simple example that’s relevant today is the new specialty therapy for migraine prevention. A plan can assess how many individuals would clinically qualify based on the current profile of migraine claimants.
  • How many new diabetic patients will emerge in each of the next three years and how will that impact treatment costs within the plan?
  • How saturated is the plan with other individual age-related chronic conditions based on it’s existing disease state and demographic profile? How will under-saturation in certain areas manifest in the next 12 months?
  • What will be the impact of increasing or decreasing the coordination of benefits with public plans given the geography of members, existing therapy, age and formulary status? How will the expected movement of claimants into and out of the plan impact that number?

Read: Options for getting the best value from drug plan spending

  • What will be the impact in 2019 of the new generic drug prices introduced in April 2018?
  • In Ontario, what will be the impact of adding OHIP+ claims back in that would have disappeared from the experience in 2018 but are expected to resurface in plans before the end of the first quarter of 2019?

As this isn’t an exhaustive list, there are certainly other variables that can be added into the predictive factors that need assessing for a given group. But it does highlight how rich plan data is and what kind of asset it can be from a planning perspective.

Mike Sullivan (msullivan@cubichealth.ca) is president of Cubic Health, an analytics and drug plan management company based in Toronto. Follow Mike on Twitter at @cubichealth.

These are the views of the author and not necessarily those of Benefits Canada.

Copyright © 2019 Transcontinental Media G.P. Originally published on benefitscanada.com

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See all comments Recent Comments

Charles Spina:

Good article.

It’s a fairly straightforward actuarial exercise to predict period-to-period drug claim cost changes, and it’s certainly best consulting practice to devise reliable (i.e. best fit) formulas for this purpose, for which the author presumably has his own proprietary model.

Contrary to his assertion, though, historical data, especially for an 80,000 member plan population, is vital to those models’ predictive value. Only then can the relevant variables and their correlations be understood.

It would have been helpful if Mike had disclosed his reasons for predicting the cost reduction. Not saying it’s the case here, but the easiest back-of-the-napkin method of predicting cost changes with a reasonable degree of confidence is to make plan changes (e.g. co-pays, formulary deletions etc.) that have universal application to the member base. If that was what led to the cost reduction prediction, it isn’t highly sophisticated analytics.

Historical data is certainly needed to aid predictive modeling. Sometimes simple simulation using one of the drug claims payor’s “what if” applications (i.e. running last year’s data through the adjudication logic of pre/post plan changes) is all that is needed to calculate up a fairly reliable output value, before adjustments for regulatory and demographic changes. Having expertise with progressive or regressive med. consumption patterns for different disease states, which Mike has, would certainly be an advantage.

Tuesday, January 29 at 12:05 pm | Reply

Mike Sullivan:

Hi Charles – thanks for the thoughtful commentary. A few quick points: we are limited to 700 words, so it doesn’t allow for a deep dive.

I want to clarify something – yes you absolutely need historical data – but not for the trend, you need it to quantify disease state saturation relative to the demographic profile of the group, and you need it to determine disease severity profile. Those are critical to meaningful predictive analysis in health claims.

With respect to the case used – no the decline was not related to a plan design change or decrease in headcount. It was driven by a a few factors – one of which was enhanced COB with public plans (which can be forecasted if you track formulary status of claims and age/geography of claimants), and a change in mix of acute vs specialty claims. There are a few other contributors, but those are key aspects in this case.

The challenge for many in this area is that many common therapies can be used for multiple indications. If you have no way of isolating what a claimant is using a specific therapy for, and the severity of the condition, you can’t quantify disease prevalence and cannot match against demographics to determine future exposure and estimate timing.

Happy to chat about it in more depth. Don’t ever hesitate to reach out. Thanks again for the thoughtful questions and commentary. I hope your 2019 is off to a great start.

Thursday, January 31 at 3:12 pm

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