Regulating the Medical Loss Ratio: Implications for the Individual Market

March 22, 2011
Jean M. Abraham, PhD

,
Pinar Karaca-Mandic, PhD

Volume 17, Issue 3

This study investigates the potential impact of new medical loss ratio regulation on the individual market for health insurance in the United States.

Objective:

To provide state-level estimates of the size and structure of the US individual market for health insurance and to investigate the potential impact of new medical loss ratio (MLR) regulation in 2011, as indicated by the Patient Protection and Affordable Care Act (PPACA).

Study Design:

Using data from the National Association of Insurance Commissioners, we provided state-level estimates of the size and structure of the US individual market from 2002 to 2009. We estimated the number of insurers expected to have MLRs below the legislated minimum and their corresponding enrollment. In the case of noncompliant insurers exiting the market, we estimated the number of enrollees that may be vulnerable to major coverage disruption given poor health status.

Results:

In 2009, using a PPACA-adjusted MLR definition, we estimated that 29% of insurer-state observations in the individual market would have MLRs below the 80% minimum, corresponding to 32% of total enrollment. Nine states would have at least one-half of their health insurers below the threshold. If insurers below the MLR threshold exit the market, major coverage disruption could occur for those in poor health; we estimated the range to be between 104,624 and 158,736 member-years.

Conclusion:

The introduction of MLR regulation as part of the PPACA has the potential to significantly affect the functioning of the individual market for health insurance.

(Am J Manag Care. 2011;17(3):211-218)

Estimation of the number of insurers expected to have medical loss ratios (MLRs) below the legislated minimum and their corresponding enrollment indicated the following:

  • In 9 states, at least 50% of health insurers would likely fail to meet the 80% minimum MLR.

  • In 12 states, at least one-half of total member-years of enrollment were affiliated with health insurers under the minimum threshold.

  • If market exit is pursued by insurers not in compliance, coverage disruption could occur for those in poor health.

A central focus of the Patient Protection and Affordable Care Act (PPACA) of 2010 is to improve the functioning of the individual and small employer group markets for health insurance through increased regulation. One provision includes establishing minimum medical loss ratios (MLRs), which represent the percentage of a health insurer’s premium revenues that are paid out for clinical services. The PPACA establishes minimum MLRs of 80% for the small group (1-100 workers) and individual markets for health insurance, and 85% for the fully insured large group market beginning January 1, 2011.1 Insurers that have MLRs below the minimum threshold are required to provide a rebate to enrollees equal to an amount that reflects the premium revenue corresponding to the difference between its actual MLR and the minimum requirement.

A primary motivation behind this regulation is to ensure that premiums overwhelmingly reflect costs associated with enrollees’ receipt of clinical services, rather than excess profitability or administrative costs that provide little direct value to consumers.2,3 However, multiple stakeholders are concerned about how insurers will respond to this new regulation and what, if any, consequences it may have for individuals’ access to coverage. Of concern is whether the individual market, which currently serves approximately 7% of individuals younger than 65 years, will become less stable (eg, insurer exit, closed blocks of business, increased barriers to access).4 Using annual filing data from the National Association of Insurance Commissioners (NAIC), we provide estimates of the size and structure of the individual market for health insurance, as well as MLRs across states over the 2002-2009 time period. With the 2009 data, we generated state-level estimates of the number of individual market insurers expected to fall below the minimum threshold and their corresponding enrollment. Finally, we estimated the amount of individual market enrollment associated with insurers falling below the minimum MLR threshold that may be vulnerable to major coverage disruption due to poor health status. We conclude with a discussion of how these findings can inform policymaking activities relating to implementation and individual market functioning.

APPROACH AND METHODOLOGY

Data

eAppendices A, B, C, D

E

Our primary data source was the NAIC Statistical Compilation of Annual Statement Information for Health Insurance Companies for 2002-2009. (The NAIC annual statements are subject to both an annual audit from a certified public accountancy firm and an examination from the state at least every 5 years. Additionally, there is a dynamic of restating balance sheet items in various levels of detail and categorizations, which allows for cross-checking of data for reporting consistency. Finally, not only are the NAIC data reviewed by the domiciliary regulator, but also other states in which the insurer writes business may review a company’s reports.) Although these data are regularly used by state regulators and industry leaders, they are limited in 2 aspects. First, the vast majority of insurers operating within California are regulated by the California Department of Managed Health Care and do not file with the NAIC. Second, approximately 20% of premiums for comprehensive major medical policies in the individual market are written by life insurers, which do not file state-level information on enrollment, premiums, and claims specific to comprehensive major medical policies in the individual market. (For more details, please refer to , and , available at www.ajmc.com.)

Measures

The unit of analysis is a company-state observation. (For example, United Healthcare of Tennessee would be distinct from United Healthcare of Colorado.) From the NAIC data, we observed or constructed the following:

State. Identifier for state in which a company operates.

Individual market member-years. Total member-months of coverage provided by an insurer within a state at the end of the calendar year divided by 12.

Incurred claims. Paid claims plus the change in claim reserves.

Change in contract reserves. Change in financial reserves held by an insurer to pay claims that are expected to be incurred under a contract after the valuation.5

Earned premiums. Direct written premiums plus the change in unearned premium reserves and reserve for rate credits.

Medical loss ratio. The ratio of incurred claims plus the change in contract reserves to earned premiums for the company-state observation, multiplied by 100 to convert it to a percentage.

Analyses

The first set of analyses characterized the individual market across states and over time. We estimated the number of health insurers operating in each state for years 2002, 2005, and 2009, as well as estimated enrollment expressed in member-years. We also examined average MLRs within states and over time and estimated coefficients of variation to investigate whether certain states experienced relatively more or less variation over the 2002 to 2009 time period. In constructing these measures, we weighted each insurer’s contribution based on its share of enrollment in the state.

Second, we estimated the number of insurers that would have MLRs under the 80% minimum and their corresponding enrollment using the historical MLR definition as well as an “adjusted” measure to reflect changes specified within the interim final rule published by the US Department of Health and Human Services. Specifically, one modification calls for re-characterizing an insurer’s expenses for certain quality-improvement activities (eg, investments to promote evidence-based medicine and patient safety, disease management, wellness programs) to be counted as clinical benefits. The other proposed change is to remove federal and state taxes and licensing or regulatory fees from premiums.6 Since insurer filings currently lack detailed information on quality-improvement expenses, some uncertainty exists with respect to the overall effect of these modifications. However, anecdotal evidence suggests a possible upward shift on MLRs on the order of 5 percentage points.7 Therefore, our adjustment increased each insurer’s historical MLR upward by 5 percentage points.

Finally, we provided an estimate of the amount of individual market enrollment associated with insurers falling below the minimum MLR threshold that may be vulnerable to major coverage disruption due to poor health status. If regulation induces insurers to exit the market, a small percentage of enrollees with high claims experience or preexisting conditions could face disruption in the short run, particularly in states that permit medical underwriting. We began by identifying spending information for the population of individuals enrolled in state high-risk pools across the United States. These consumers are likely similar to those who might be vulnerable to coverage disruption within the individual market. Data from a recent study by the General Accounting Office8 report average paid claims of $9437 for this population in 2008. Next, we used information on annual insurer-paid spending for the nonelderly population with individual market coverage from the 2005-2007 Medical Expenditure Panel Survey (MEPS) Household Component to estimate the proportion of individuals with spending in excess of $9437. (Insurer- paid spending was inflated to 2008 dollars to align with the Government Accountability Office study findings.) We estimated this proportion to be .048. (There is well-documented evidence that spending is underreported in the MEPS. We used the adjustment to inflate expenditures by 21%.9) Lastly, we multiplied aggregate enrollment among insurers that have MLRs below the 80% minimum requirement for each state based on the NAIC data analysis by this proportion to estimate the potential enrollment vulnerable to major coverage disruption due to being medically uninsurable.

RESULTS

Table 1

summarizes the size and structure of the individual market by state for years 2002, 2005, and 2009. In 2009, we observed enrollment of 6.7 million member-years within 371 health insurance company-state observations. These estimates did not include any insurers operating in California or organizations that file as life insurers. (Please refer to eAppendix D for state-level estimates of life insurers selling comprehensive major medical policies.)

Not surprisingly, the number of health insurers with active operations varied widely across states, with more populated states having a larger number of insurers. In 2009, 5 states (Florida, New York, Michigan, Pennsylvania, and Ohio) each reported at least 15 insurers. In contrast, 10 states had 3 or fewer health insurers (Alabama, Mississippi, Vermont, Alaska, Delaware, Hawaii, North Dakota, New Hampshire, Rhode Island, and Wyoming). Over the 2002 and 2009 time period, most states experienced an increase in the number of health insurers and modest enrollment growth.

It is important to recognize that within states, insurers vary extensively in size, with some having a very small enrollment. As indicated in the interim final rule, insurers with fewer than 1000 member-years in a given state for 2011 would be considered to have a “non-credible MLR,” a classification that would exempt them from paying the rebate if the MLR is below the minimum. In 2009, 179 of the 371 company-state observations reported fewer than 1000 member-years. For 21 states, this additional condition reduced the number of health insurers with “credible MLRs” by more than 50%.

Within our data, some states were missing information, particularly for calendar year 2002. In other states (eg, Alabama, Illinois, Indiana), we observed implausibly large changes in enrollment between 2002 and 2009. These estimated changes in enrollment likely reflected changes in reporting behavior or state regulatory requirements, rather than actual market growth.

Table 2

provides state-level estimates of average MLRs, as well as the coefficient of variation for the 2002 to 2009 period. (For the analyses in Table 2, we performed an outlier analysis given implausibly low and high values for premiums and claims. We excluded observations that were in the bottom 1% of both claims incurred and premiums earned, and those in the top 1% of both claims incurred and premiums earned, as well as those in the bottom and top 1% of the MLR.) These estimates are an aggregate representation of insurers’ performance for each state. We observed extensive variation. Health insurers within New Hampshire had the lowest enrollment-weighted average MLR (.629). In contrast, 4 states (Alabama, Massachusetts, Michigan, and North Dakota) had enrollment-weighted average MLR values in excess of 1.0. This can happen if 1 or more large insurers within a state incur claims experience that exceeds the amount of premiums earned for that year.

Table 3

summarizes the 2009 data on the number of insurers and corresponding enrollment population potentially affected by the new minimum MLR requirement. Column 1 shows the number of health insurers with active operations by state. Columns 2 and 3 identify those insurers with MLRs under the 80% minimum (based on the historical MLR definition) and their corresponding enrollment. Columns 4 and 5 provide analogous estimates, but use the PPACA-adjusted MLR definition described above. Columns 6 and 7 provide upper-bound and lower-bound estimates of market enrollment potentially vulnerable to coverage disruption for medical reasons in the worst case that insurers who fail to meet the new MLR regulation choose to exit the market.

In Table 3, column 2, we note that 146 of the 371 company-state observations were below the 80% minimum MLR requirement. In 21 states, at least 50% of health insurers would not meet the minimum threshold. Allowing for the 5 percentage point upward adjustment to the MLR, the number of insurers who would not meet the minimum threshold declined to 106; only 9 of the 21 states (Arkansas, Illinois, Louisiana, Nebraska, New Hampshire, Oklahoma, Rhode Island, Wyoming, and West Virginia) would continue to have at least one-half of their health insurers below the threshold.

In 2009, health insurers filing with the NAIC reportedalmost 6.7 million member-years of enrollment (see Table 1, column 6). Approximately 3.3 million member-years were associated with insurers that had MLRs that fell under the 80% threshold, based on the historical MLR definition. Using the PPACA-adjusted MLR definition reduced the aggregate enrollment impact to 2.18 million member-years. Twelve states had at least 50% of their enrollment affiliated with health insurers under the 80% threshold (Arkansas, Arizona, Florida, Illinois, Indiana, New Hampshire, Nevada, South Carolina, Tennessee, Texas, Virginia, and West Virginia). Together, insurers within these states had 1.87 million member-years (28% of total reported US enrollment) concentrated among health insurers with MLRs below the minimum threshold, even after allowing for the 5 percentage point upward adjustment to the historical MLR definition.

Insurers with MLRs that fall below the minimum requirement may respond in a variety of ways, including cutting administrative expenses related to marketing or distribution, lowering premiums, using less aggressive medical management, dropping product lines that may contribute to lower MLRs, or exiting the market. (High-deductible health plans generally have much lower MLRs than traditional health maintenance organizations [HMOs] or preferred provider organizations [PPOs]. The interim final rule includes an additional adjustment in the calculation of the MLR for insurers that sell plans with high deductibles.) Should insurers pursue either product line elimination or market exit, consumers could experience coverage disruptions. For enrollees with recently diagnosed, serious medical conditions or high claims experience, these types of responses could make them particularly vulnerable. This is particularly true for states that permit medical underwriting. Of course, in 2014, guaranteed issue provisions will go into effect for plans offered within Exchanges, thereby mitigating some of these potentially adverse consequences.

Columns 6 and 7 in Table 3 provide an estimated range of enrollees vulnerable to coverage disruption because of health status, calculated using our results for enrollment among insurers that have MLRs below 80% and our estimated proportion of the individual market population considered “medically uninsurable” (.048). We estimated the range to be 104,624 to 158,736 member-years. Removing from consideration enrollment associated with states already having guaranteed issue reduced the upper bound by only 3041 member-years (fewer than 2%). (These estimates assumed a constant proportion of total enrollment with high claims across states. In the MEPS, we found little difference in the fraction of enrollees in excess of $9437 by census region [Northeast, Midwest, South, and West].) In absolute terms, states with the largest levels of vulnerable enrollment include Arizona, Florida, South Carolina, Texas, and Virginia—states that tend to have more limited consumer protections with respect to purchasing coverage within the individual market.10

DISCUSSION

The introduction of a minimum MLR regulation as part of the PPACA has the potential to significantly affect the functioning of the individual market. Our results suggest that in 9 states, at least 50% of actively operating health insurers would likely fail to meet the 80% minimum MLR even after allowing for a 5 percentage point upward adjustment to account for changes in the way the measure will be calculated relative to the historical definition. Furthermore, we observed that in 12 states, at least one-half of total member-years of enrollment were affiliated with health insurers failing to meet the minimum MLR using the PPACA-adjusted definition.

The implications of MLR regulation remain uncertain, since one cannot accurately predict insurers’ strategic responses. To the extent that insurers with low MLRs opt for terminating product lines or exiting the market completely, consumers could experience some coverage disruption. Individual market enrollees who are medically uninsurable due to a recent diagnosis and/or high claims experience may be particularly vulnerable, with the largest numbers in Arizona, Florida, South Carolina, Texas, and Virginia. Of course, the effects of the MLR regulation on enrollees’ experiences in the short run could vary extensively given differences across states in individual market regulations pertaining to guaranteed issue, designated insurers of last resort, and premium rating, as well as individuals’ access to state-based high-risk pools. In the longer run, it will be important to investigate how MLR regulation interacts with the creation of state-based Exchanges, the latter of which are hypothesized to lower insurers’ administrative costs.

Two study limitations are worth noting. First, while the NAIC data are the most comprehensive available, insurers in California are not represented. In addition, coverage sold by other types of insurers, notably life insurers, are not included since they do not file the information necessary for estimating comparable MLRs at the state level. In eAppendix C, we present an indirect method for identifying the presence of these insurers using alternative NAIC filing data sources. Results from these supplemental analyses suggest that in certain states, these insurers may have a large market presence. As a result, Tables 1 and 2 underestimate the number of competing insurers and enrollment in each state. Moreover, the absence of data for these firms could lead to underestimation or overestimation of the impact of MLR regulation, depending on whether these firms have ratios that are above or below the 80% threshold.

Second, the NAIC data did not permit a precise accounting of the number of unique individuals in each state that are enrolled by health insurers operating with MLRs below the 80% threshold. Rather, we observed only total member years of coverage.

Policy Implications

For state regulators, it will be important to monitor insurers’ activities as they respond to the MLR regulation, particularly since compliance can be achieved in a number of ways, including reductions in administrative costs, profits, or net income; increases to claims costs; or premium reductions. Each has different implications for consumers.

As our analysis reveals, the individual market for health insurance in some states is already highly concentrated, with only a few active health insurers. For these states in particular, exit or threat of exit could be very disruptive to individuals in the short run. In such cases, state insurance commissioners could consider seeking transitional relief from the US Department of Health and Human Services, given the likelihood of market stabilization.

For federal policy makers, our analysis suggests that the potential impact of this new regulation on the individual market for health insurance could be quite large and that it will vary dramatically across states. Moreover, our analysis illustrates the significant challenges facing policy analysts and researchers in evaluating how insurance markets will be affected, given the introduction of this and other new PPACA regulations. The most notable challenge is not having a consistent, comprehensive source of data across all types of insurers writing health insurance across all US states.

Going forward, it will be important for additional investments to be made in the collection of information that can enable monitoring of both the individual and group markets with validated outcome measures. Finally, additional research is needed to better understand variation in insurers’ MLRs; the determinants of MLRs, including the role of population characteristics and provider market competition; and the impact that this new federal regulation will have on insurance market stability and long-run functioning.

Acknowledgments

We thank the Changes in Health Care Financing and Organization, an initiative of the Robert Wood Johnson Foundation, for financial support. We also thank David Vacca, CPA, Todd Sells, MBA, Dan Daveline, CPA, and Brian Webb, MPA, from the National Association of Insurance Commissioners for providing technical assistance regarding the data. Finally, we thank Jon Christianson, PhD, Roger Feldman, PhD, John Nyman, PhD, Amitabh Chandra, PhD, and Alshadye Yemane, MPP, for helpful advice.

Author Affiliations: From Division of Health Policy and Management (JMA, PK-M), School of Public Health, University of Minnesota, Minneapolis, MN.

Funding Source: Changes in Health Care Financing and Organization, Robert Wood Johnson Foundation.

Author Disclosures: The authors (JMA, PK-M) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JMA, PK-M); acquisition of data (JMA, PK-M); analysis and interpretation of data (JMA, PK-M); drafting of the manuscript (JMA, PK-M); critical revision of the manuscript for important intellectual content (JMA, PK-M); statistical analysis (JMA, PKM); and obtaining funding (JMA, PK-M).

Address correspondence to: Jean M. Abraham, PhD, Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 729, Minneapolis, MN 55455. E-mail: abrah042@umn.edu.

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