Transitional policies result in higher medical loss ratios

A new Milliman analysis shows that the percentage of transitional policy members in a state’s health exchange market appears to correlate with higher medical loss ratios. In the analysis, Milliman consultants Erik Huth and Jason Karcher quantify the effect that transitional policies had on issuers’ 2014 individual market performances and how it may result in 2017 rate increases for transitional states.

Here’s an excerpt:

The table in Figure 3 shows that issuers in transitional states had higher 2014 loss ratios but appear to not have taken large enough 2015 and 2016 rate increases to achieve profitable 2016 loss ratios (assuming 2014 to 2016 significant cost savings are not realized in other ways). Although issuers’ 2017 rate increases will reflect their 2015 experience and updated projections, there is potential for transitional states to see higher rate increases in 2017.

Figure 3

The graph in Figure 4 shows the 2014 ACA loss ratio and the average 2014 to 2016 statewide QHP base rate change for each state. The gray line represents an illustrative 2014 to 2016 rate increase needed to target an 85% 2016 loss ratio given the 2014 loss ratio and assuming a 5% annual claim trend. For example, a state with an 85% 2014 loss ratio would require a 10.25% 2014 to 2016 rate increase to target an 85% 2016 loss ratio (i.e., 5% annual rate increases to cover the 5% annual claim trend to maintain the 85% loss ratio). States well underneath the line indicate a possible need for higher 2017 increases than states closer to the line. Keep in mind that projected 2016 loss ratios are merely illustrative. There are many factors that will affect a state’s overall 2016 loss ratio and required 2016 and 2017 rate increases, such as, but not limited to, changes in experience and statewide morbidity levels, wear-off of pent-up demand, provider contracting, claim trends, population migration and characteristics, and product and issuer mix. These values also represent a statewide composite, while specific issuers could have materially different results than the average.

Figure 4

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