Medicare Advantage: Substantial Excess Payments Underscore Need for CMS to Improve Accuracy of Risk Score Adjustments
Published: 2013-01-31T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: CMS pays plans in MA--the private plan alternative to FFS--a predetermined amount per beneficiary adjusted for health status. To make this adjustment, CMS calculates a risk score, a relative measure of expected health care for each beneficiary. Risk scores should be the same among all beneficiaries with the same health conditions and demographic characteristics. Differences in diagnostic coding between MA plans and Medicare FFS led to inappropriately high MA risk scores and payments to MA plans, and CMS adjusted for coding differences in 2010. In January 2012, GAO reported that CMS's adjustments to risk scores did not sufficiently correct for coding differences, resulting in excess payments to MA plans. Since completing the analysis for the January 2012 report, risk score data for two additional years have become available. GAO (1) determined the extent to which differences, if any, in diagnostic coding between MA plans and Medicare FFS affected MA risk scores and payments to MA plans in 2010, 2011, and 2012; and (2) identified what changes, if any, CMS made to its risk score adjustment methodology for 2013 and intends to make for future years. To do this, GAO compared risk score growth for MA beneficiaries with an estimate of what risk score growth would have been for those beneficiaries if they were in Medicare FFS for 2010 and projected the growth to 2011 and 2012, and determined if there were changes to CMS's methodology by reviewing agency documentation and interviewing agency officials.
section_title: What GAO Found
paragraphs: GAO found that the cumulative impact of coding differences on risk scores increased from 2010 through 2012 and was greater than the Centers for Medicare & Medicaid Services' (CMS) risk score adjustment of 3.4 percent for each of the 3 years. In updating the analysis from its January 2012 report, GAO estimated that cumulative Medicare Advantage (MA) risk scores in 2010 were 4.2 percent higher than they likely would have been if the same beneficiaries had been enrolled continuously in Medicare fee-for-service (FFS). For 2011, GAO estimated that differences in diagnostic coding resulted in risk scores that were 4.6 to 5.3 percent higher than they likely would have been if the same beneficiaries had been continuously enrolled in FFS. This upward trend continued for 2012, with estimated risk scores 4.9 to 6.4 percent higher.
CMS's adjustment to risk scores for 2010 through 2012 to account for diagnostic coding differences was too low, resulting in estimated excess payments to MA plans of at least $3.2 billion. CMS's annual 3.4 percent reduction in risk scores is equivalent to $2.8 billion in 2010, $3.0 billion in 2011 and $3.2 billion in 2012. According to GAO's estimates, the amount of the excess payments to MA plans after accounting for CMS's adjustments was $0.6 billion in 2010, between $1.1 billion and $1.6 billion in 2011, and between $1.5 billion and $2.9 billion in 2012. Cumulatively across the 3 years, this equals excess payments of between $3.2 billion and $5.1 billion.
For 2013, CMS continues to use the risk score adjustment of 3.4 percent it used in 2010, 2011, and 2012. To conduct its data-based analysis, CMS officials reported that they used the same methodology used in 2010, but they incorporated more recent data. CMS officials told us that, in addition to the results of the data analysis, they incorporated additional factors such as recent payment changes made to the MA program under the Patient Protection and Affordable Care Act and the maintenance of benefits for seniors. The Social Security Act does not prescribe CMS's methodology for adjusting for differences in diagnostic coding. However, the express purpose of the requirements to conduct and incorporate into the risk scores a data-based analysis of coding differences is to ensure payment accuracy. The act does not provide for factors other than the results of the analysis to be incorporated into the adjustment, suggesting that accuracy would be ensured solely through the incorporation of analytical results. CMS officials stated that they believed there was policy discretion with respect to the most appropriate adjustment factor but did not identify the specific source of their authority to consider factors other than the required data analysis when determining the adjustment amount. While CMS did not change its risk score adjustment methodology for 2013, agency officials said they may revisit their methodology for future years.
GAO's findings underscore the importance for CMS to implement the recommendation from GAO's January 2012 report that the agency improve the accuracy of its MA risk score adjustments by taking steps such as using the most current data available and incorporating adjustments for additional beneficiary characteristics.
CMS reviewed a draft of this report and stated that it had no comments.

section_title: Letter
paragraphs: In 2012, the federal government will spend an estimated $134.7 billion on  the Medicare Advantage (MA) program, an alternative to the original  Medicare fee-for-service (FFS) program, in which private health plans  offer health care coverage to Medicare beneficiaries. The Centers for  Medicare & Medicaid Services (CMS), the agency that administers  Medicare, pays private plans a monthly amount per beneficiary to provide  health care services for each beneficiary enrolled in these plans. CMS  adjusts this payment to account for a beneficiary’s health status, a  process known as risk adjustment, and assigns to each MA beneficiary a  risk score—a relative measure of expected health care costs.
Risk scores for beneficiaries with the same diagnoses and characteristics  should be identical, regardless of whether the beneficiaries are in an MA  plan or Medicare FFS. However, MA plans have a greater incentive than  FFS providers to make sure that all medical diagnoses are  comprehensively coded, as this can increase beneficiaries’ risk scores  and ultimately the payments plans receive. This is not the case for FFS  providers, who are generally paid for the services they provided and  whose payments do not vary based on the diagnoses of the beneficiaries   Consequently, risk scores for beneficiaries in  who received the services.MA plans may tend to be higher relative to the risk scores of beneficiaries  in FFS who are in similar health and have identical characteristics. To  ensure that MA plans are not overpaid as a result of these differences in  diagnostic coding patterns, CMS makes a separate adjustment to MA  plan payments.
In January 2012, we reported that CMS’s adjustments to account for  differences in diagnostic coding were inadequate and resulted in excess  payments to MA plans. In 2010, CMS estimated that 3.4 percent of MA  beneficiary risk scores were attributable to diagnostic coding differences  between MA plans and Medicare FFS providers. CMS reduced MA  beneficiaries’ 2010 risk scores by 3.4 percent, which resulted in the  agency saving $2.7 billion in excess payments to MA plans. However, on  the basis of the information available when we did our study, we  estimated that the difference in diagnostic coding between MA plans and  Medicare FFS providers was greater—4.8 percent to 7.1 percent— depending on the assumptions made about trends in risk score growth.  Our estimate differed from CMS’s because we used more recent data,  incorporated the trend of coding differences over time, and accounted for  additional beneficiary characteristics.
We also found that the impact of coding differences increased over time  and noted that this trend suggested that the cumulative impact of coding  differences in 2011 and 2012 could be larger than in 2010. However,  CMS applied the same 3.4 percent adjustment to risk scores it used in  2010 to risk scores in 2011 and 2012. To help ensure appropriate  payments to MA plans, we recommended that CMS take steps to improve  the accuracy of the adjustment made for differences in diagnostic coding  practices between MA plans and Medicare FFS.
Since we completed the analysis for our January 2012 report, risk score  data for 2 additional years have become available. You asked us to  update and extend our work on differences in diagnostic coding practices  between MA and Medicare FFS and their impact on MA payments. This  report (1) determines the extent to which differences, if any, in diagnostic  coding between MA plans and Medicare FFS affected MA risk scores and  payments to MA plans in 2010, 2011, and 2012; and (2) identifies what  changes, if any, CMS made to its risk score adjustment methodology for  2013 and intends to make for future years.
To determine the extent to which differences in diagnostic coding  between MA plans and Medicare FFS affected MA risk scores for 2010,  2011, and 2012, we used a similar approach as for our January 2012  report. We first compared actual risk score growth for beneficiaries in MA  plans with the estimated risk score growth MA beneficiaries would have  had if they were enrolled in Medicare FFS. To do this, we calculated  changes in disease scores—the portion of the risk score that is based on  a beneficiary’s coded diagnoses—for MA beneficiaries and used  regression analysis to estimate what changes in disease scores would  have been if those beneficiaries were enrolled in Medicare FFS. We  attributed differences between actual and estimated disease score growth  to differences in coding practices between MA plans and Medicare FFS.  We estimated the extent to which differences in diagnostic coding  between MA plans and Medicare FFS affected 2010, 2011, and 2012 risk  scores by estimating the cumulative impact of coding differences starting  with 2007 risk scores.
Because 2010 risk scores were the most recent available at the time of  our analysis for this report, we projected the estimated impact of coding  differences to 2012 using different assumptions of trends: the lower  projection assumed that the impact of coding differences on risk score  growth for 2010 to 2012 was the same as it was for 2009 to 2010, while  the higher projection assumed that the trend in the impacts of diagnostic  coding differences on risk score growth from 2005 through 2010  continued through 2012. To calculate the cumulative impact of coding  differences on 2010, 2011, and 2012 MA risk scores, we summed the  impacts over the previous periods, weighted by the proportion of the MA  population that could have experienced risk score growth due to coding  differences during that period.
We made two enhancements to the methodology used for our January  2012 report. First, we altered our assumption about which beneficiaries  had risk score growth that could be affected by differences in coding  practices between MA plans and Medicare FFS. Specifically, for our  January 2012 report, we assumed that beneficiaries who were recently  enrolled in MA without any prior Medicare FFS enrollment experienced  the same risk score growth due to diagnostic coding differences as  beneficiaries who were enrolled in Medicare FFS or MA the previous  year. However, enrollees who are new to the Medicare program tend to  be healthier than other beneficiaries and may have less contact with their  health plan physicians and less information in their medical records that  plans could use to adjust risk scores. Therefore, for this report, we  instead made the more conservative assumption that beneficiaries new to  Medicare and enrolled in MA did not have risk score growth that could be  affected by differences in coding practices between MA plans and  Medicare FFS. Second, we adjusted both our lower and higher  cumulative estimates to account for the effect of beneficiaries who died  after June 30, 2010, or otherwise left MA since 2010. For our January  2012 report, we did not make this adjustment. We determined this  adjustment was needed for the current report after observing that the  beneficiaries who left MA or died had, on average, a greater difference  between their actual and estimated disease scores than those  beneficiaries who remained in MA.
To quantify the cumulative impact of both our and CMS’s estimates of  coding differences on payments to MA plans, we estimated the risk score  growth attributable to coding differences, as described above. We then  used data on MA plan bids—plans’ proposed reimbursement rates for the  average beneficiary—to calculate total risk-adjusted payments for each  MA plan before and after applying a coding adjustment. Finally, we  calculated the difference between the two payment amounts.
We analyzed data from CMS on Medicare beneficiaries, including data  collected from Medicare providers and MA plans. We assessed the  reliability of the CMS data we used by interviewing officials responsible  for using these data to determine MA payments, reviewing relevant  documentation, and examining the data for obvious errors. We  determined that the data were sufficiently reliable for the purposes of our  study. See appendix I for more details on our scope and methodology.
To describe changes, if any, CMS made to its coding adjustment  methodology for 2013 and intends to make for future years, we reviewed  CMS documents and interviewed CMS staff. In particular, we indentified  the extent to which CMS planned to update its methodology to  incorporate recent data, additional beneficiary characteristics, and  account for trends in practice pattern disparities.
We conducted this performance audit from March 2012 through January  2013 in accordance with generally accepted government auditing  standards. Those standards require that we plan and perform the audit to  obtain sufficient, appropriate evidence to provide a reasonable basis for  our findings and conclusions based on our audit objectives. We believe  that the evidence obtained provides a reasonable basis for our findings  and conclusions based on our audit objectives.
subsections: section_title: Background
paragraphs: The Social Security Act requires that payments to MA plans be adjusted  for variation in the cost of providing health care to beneficiaries on the  basis of various risk factors, including health status. For most MA  beneficiaries, CMS uses its CMS-Hierarchical Condition Categories  (CMS-HCC) model to risk-adjust payments to MA plans. HCCs, which  represent major medical conditions, are groups of medical diagnoses  where related groups of diagnoses are ranked on the basis of disease  severity and cost. The CMS-HCC risk adjustment model uses enrollment  and claims data from Medicare FFS. The model uses beneficiary  characteristic and diagnostic data from a base year to calculate the  expected health care expenditures under Medicare FFS for the following  year.demographic data for 2009 to determine the risk scores used to adjust  payments to MA plans in 2010.
For example, CMS used MA beneficiary diagnostic and  The Social Security Act further directs CMS to ensure that adjustments to  payments for health status reflect changes in treatment and coding  practices in Medicare FFS and differences in coding patterns between  MA plans and providers under Medicare Parts A and B to the extent  differences are identified. To ensure payment accuracy, the act requires  CMS to conduct an annual analysis of these coding differences in time to  incorporate the results into the risk scores for each year. In conducting  such an analysis, CMS is required to use data submitted with respect to  2004 and subsequent years as such data are available. CMS is required  to continue adjusting risk scores for coding differences until the time it is  able to implement risk adjustment using MA diagnostic, cost, and use  data. Beginning with 2014, the diagnostic coding adjustments are subject  to a statutory minimum, which was recently amended by the American  Taxpayer Relief Act of 2012. Specifically, the Social Security Act, as  amended, requires CMS to reduce MA risk scores by at least   1.5 percentage points more than the 2010 adjustment (a total of   4.9 percent) for 2014 and increases the annual minimum percentage  reduction to not less than 5.9 percent for 2019 and subsequent years.
CMS did not adjust MA risk scores in 2008 or 2009, the first years for  which a diagnostic coding adjustment was required under the Deficit  Reduction Act of 2005. However, for 2010 CMS estimated that   3.4 percent of MA beneficiary risk scores were attributable to differences  in diagnostic coding over the previous 3 years and reduced MA  beneficiaries’ 2010 risk scores by 3.4 percent. To calculate this  percentage, CMS estimated the annual difference in disease score  growth between MA and Medicare FFS beneficiaries for three different  groups of beneficiaries who were either enrolled in the same MA plan or  in Medicare FFS from 2004 to 2005, 2005 to 2006, and 2006 to 2007.  CMS accounted for differences in age and mortality when estimating the  difference in disease score growth between MA and Medicare FFS  beneficiaries for each period. Then, CMS calculated the average of the  three estimates. To apply this average estimate to 2010 MA  beneficiaries,
CMS multiplied the average annual difference in risk score growth by  its estimate of the average length of time that 2010 MA beneficiaries  had been continuously enrolled in the same MA plans over the  previous 3 years, and
CMS multiplied this result by 81.8 percent, its estimate of the  percentage of 2010 MA beneficiaries who were enrolled in an MA plan  in 2009 and therefore were previously exposed to MA coding  practices.
CMS applied the same 3.4 percent adjustment to risk scores it used in  2010 to risk scores in 2011 and 2012.
section_title: CMS’s Adjustment for Coding Differences for 2010 through 2012 Was Too Low, Resulting in Estimated Excess Payments to MA Plans of at Least $3.2 Billion
paragraphs: CMS’s risk score adjustment for diagnostic coding differences for 2010  through 2012 was too low. Specifically, we estimated that the cumulative  impact of coding differences on risk scores increased from 2010 through  2012 and was greater than CMS’s risk score adjustment of 3.4 percent for  each of the 3 years. In updating our analysis from the January 2012  report, we estimated that cumulative risk scores in 2010 were 4.2 percent  higher than they likely would have been if the same beneficiaries had  been enrolled continuously in FFS. Using the methodology described  earlier, we estimated that differences in diagnostic coding resulted in  2011 risk scores that were 4.6 to 5.3 percent higher than they likely would  have been if the same beneficiaries had been continuously enrolled in  FFS. This upward trend continued for 2012, with estimated risk scores   4.9 to 6.4 percent higher (see fig. 1).
Because CMS’s adjustment to risk scores for 2010 through 2012 to  account for diagnostic coding differences was too low, we estimated that  MA plans received excess payments of between $3.2 billion and   $5.1 billion over the 3-year period. CMS’s annual 3.4 percent reduction in  risk scores is equivalent to reducing total payments to MA plans by an  estimated $2.8 billion in 2010, $3.0 billion in 2011 and $3.2 billion in 2012.  According to our estimates, the amount of the excess payments to MA  plans after accounting for CMS’s adjustments was $0.6 billion in 2010,  $1.1 billion to $1.6 billion in 2011, and $1.5 billion to $2.9 billion in 2012.  (See fig. 2.)
section_title: CMS Did Not Change Its Risk Score Adjustment Methodology for 2013, but May Revisit It in Future Years
paragraphs: According to CMS officials, the agency used the same methodology to  determine the 2013 risk score adjustment as it used in 2011 and 2012,  resulting in a risk score adjustment of 3.4 percent for 2013. CMS  conducted a data-based analysis of coding differences for 2013 and  considered the results, along with other factors, in determining the  adjustment provided for in the Social Security Act. To conduct its data- based analysis, CMS officials reported that they used the same  methodology they used to calculate the 3.4 percent adjustment for 2010,  but incorporated more recent data. In addition to the results of their  data-based analysis, CMS officials told us that they took into account  other factors when determining the 2013 risk score adjustment, such as  payment changes made to the MA program under the Patient Protection  and Affordable Care Act, the stability of the MA program, and the  maintenance of benefits for seniors. CMS’s risk score adjustment of   3.4 percent for 2013 is the same that it used in 2010, 2011, and 2012.
The Social Security Act does not prescribe the methodology that CMS is  to use in adjusting for differences in diagnostic coding and, in this regard,  it provides the agency with discretion in designing and conducting its  analysis of coding differences. However, the express purpose of the  requirements to conduct and incorporate a data-based analysis of coding  differences into the risk scores is to ensure payment accuracy. The  statute does not provide for factors other than the results of the analysis  to be incorporated into the adjustment, suggesting that accuracy would be  achieved through the incorporation of these analytical results by  themselves. In response to our inquiries, CMS officials told us that they  reviewed a range of options when determining an adjustment for 2013,  but did not explain whether these options were derived from the agency’s  data-based analysis. CMS officials did not identify the specific source of  their authority to consider factors other than the required data-based  analysis when determining the adjustment amount, but stated that they  believed that there was policy discretion with respect to the most  appropriate adjustment factor for the payment year. While CMS did not  change its risk score adjustment methodology for 2013, agency officials  said they may revisit their methodology for future years. While diagnostic  coding adjustments are subject to a statutory minimum beginning in 2014,  CMS may implement a diagnostic coding adjustment that is greater than  the statutory minimum if they determine the minimum is too low.
section_title: Concluding Observations
paragraphs: Risk adjustment is important to ensure that payments to MA plans  adequately account for differences in beneficiaries’ health status and to  maintain plans’ financial incentive to enroll and care for beneficiaries  regardless of their health status. Our work confirms that differences in  diagnostic coding caused risk scores for MA beneficiaries to be higher  than those for comparable beneficiaries in Medicare FFS in 2010, 2011,  and 2012. CMS’s decision to use a 3.4 percent adjustment to risk scores  for 2010 through 2012 instead of the higher adjustments called for by our  analysis resulted in excess payments to MA plans. The existence of such  excess payments indicates that CMS’s adjustment does not accurately  account for differences in treatment and diagnostic coding between MA  plans and Medicare FFS—the stated goal of the statute that required  CMS to develop a diagnostic coding adjustment. In our January 2012  report, we recommended that CMS take steps to improve the accuracy of  the adjustment to account for excess payments due to differences in  diagnostic coding. We noted that CMS could, for example, account for  additional beneficiary characteristics, include the most recent data  available, identify and account for all the years of coding differences that  could affect the payment year for which an adjustment is made, and  incorporate the trend of the impact of coding differences on risk scores.  CMS’s adjustment for 2013 is the same as it used in 2010, 2011, and  2012. However, given our finding that this adjustment was too low and  resulted in estimated excess payments to MA plans of at least   $3.2 billion, we continue to believe that it is important for CMS to  implement our recommendation that it update its methodology to more  accurately account for differences in diagnostic coding.
section_title: Agency Comments
paragraphs: We provided a draft of this report to CMS for comment. CMS did not have  any general comments. The agency provided one technical comment,  which we incorporated.
As agreed with your offices, unless you publicly announce the contents of  this report earlier, we plan no further distribution until 30 days from the  report date. At that time, we will send copies to the Secretary of Health  and Human Services, interested congressional committees, and others.  In addition, the report is available at no charge on the GAO website at  http://www.gao.gov.
If you or your staff has any questions about this report, please contact me  at (202) 512-7114 or cosgrovej@gao.gov. Contact points for our Offices  of Congressional Relations and Public Affairs may be found on the last  page of this report. GAO staff who made major contributions to this report  are listed in appendix II.
section_title: Appendix I: Scope and Methodology
paragraphs: This appendix describes the scope and methodology that we used to  address our objective to determine the extent to which differences, if any,  in diagnostic coding between Medicare Advantage (MA) plans and  Medicare fee-for-service (FFS) affected risk scores and payments to MA  plans in 2010, 2011, and 2012. This methodology includes enhancements  from the methodology we used in our January 2012 report.
subsections: section_title: Estimating the Impact of Coding Differences on MA Risk Scores
paragraphs: To determine the extent to which differences, if any, in diagnostic coding  between MA plans and Medicare FFS affected MA risk scores in 2010,  2011, and 2012, we compared actual risk score growth for beneficiaries in  our MA study population with the estimated risk score growth these  beneficiaries would have had if they had been enrolled in Medicare FFS.  For our estimates, we assumed MA plans and Medicare FFS had  equivalent coding patterns in 2006—the year of coding upon which 2007  risk scores were based—and therefore calculated the cumulative effect of  coding differences starting with risk score data from 2007. Because 2010  risk score data were the most current data available at the time of our  analysis, we projected the extent of coding differences in 2011 and 2012  on the basis of trends from 2005 through 2010. To do these analyses,  we used Centers for Medicare & Medicaid Services (CMS) enrollment  and risk score data from 2004 through 2011. Risk scores for a given  calendar year are generally based on beneficiaries’ diagnoses in the  previous year, so we identified our study population on the basis of  enrollment data for 2004 through 2009 and analyzed risk scores for that  population for 2005 through 2010.
Our MA study population consisted of a retrospective cohort of 2010 MA  beneficiaries who were enrolled in MA for all of 2009 and either MA or  FFS in 2008, and we followed them back for the length of their continuous  enrollment to 2004. Beneficiaries remained in our cohort for as many years as they were continuously enrolled in MA, plus (if applicable) the  year of Medicare FFS enrollment immediately preceding their MA  enrollment.
We calculated disease scores using the 2010 version of the CMS-HCC risk adjustment  community model (used for payment in 2010), and summed the appropriate coefficients  for each of the HCC variables. We normalized disease scores for each year to 2005 by  using the FFS normalization factor that CMS used to normalize risk scores in 2010.  Normalization keeps the average Medicare FFS risk score constant at 1.0 over time and is  necessary to compare disease scores across years. change in disease scores that would have occurred if those MA  beneficiaries had been continuously enrolled in FFS.
We then compared these estimates of disease score growth under FFS to  the MA beneficiaries’ actual disease score growth and, because the  regression model accounted for other relevant factors affecting disease  score growth, we identified the difference as attributable to coding  differences between MA and FFS. To calculate the cumulative impact of  coding differences on 2010 MA risk scores, we summed the 2007 to  2008, 2008 to 2009, and 2009 to 2010 impact estimates for our  retrospective MA cohort, weighting each impact estimate by the  percentage of 2010 MA beneficiaries that could have experienced risk  score growth due to coding differences during that period. To convert the  cumulative impact of coding differences on 2010 MA risk scores into a  percentage, we divided by the average MA risk score in 2010. To  calculate the cumulative impact of coding differences on 2011 and 2012  MA risk scores, we used a similar methodology, except that we had to  estimate the risk score growth due to coding differences for 2010 to 2011  and 2011 to 2012.
To estimate the risk score growth due to coding differences for 2010 to  2011 and 2011 to 2012, we used the estimates based on our 2010  retrospective cohorts for five time periods—2005 to 2006, 2006 to 2007,  2007 to 2008, 2008 to 2009, and 2009 to 2010—and made two different  projection assumptions. One projection, which we used to calculate our  high cumulative impact estimate, assumed that the trend over 2005 to   We also provided a more conservative  2010 continued through 2012.estimate that assumed the annual growth trend in coding differences from  2010 to 2012 stabilized at the same level as 2009-2010. We provided this  lower estimate to account for the possibility that the growth rate will flatten  as MA plans improve their systems for comprehensively coding  diagnoses.
Our updated methodology includes two major enhancements to the  methodology used for our January 2012 report. First, we altered our  methodology to make a more conservative assumption of the risk score  growth due to coding differences for beneficiaries that recently enrolled in  MA without any prior Medicare FFS enrollment. These beneficiaries are  new to the Medicare program and tend to be healthier than other  beneficiaries. Therefore, they may have less contact with their health plan  physicians and less information in their medical records that plans could  use to adjust risk scores. As a result, for MA beneficiaries that had a risk  score based on MA coding for the second year of the period but did not  have a risk score based on either MA or FFS coding for the first year of  the period, we altered our methodology to assume that these  beneficiaries had zero risk score growth due to coding differences during  the period. For the January 2012 report, we had assumed that these  beneficiaries experienced the same risk score growth due to coding  differences, on average, as did those beneficiaries for which we could  measure risk score growth due to coding differences.
Second, we adjusted our cumulative impact estimates for 2011 and 2012  to account for the effect of beneficiaries who died after June 30, 2010, or  otherwise left MA since 2010. We determined an adjustment was needed  after observing that the impacts of coding differences for the time periods  2005-2006, 2006-2007, and 2007-2008 were smaller for the 2010  retrospective cohort used for this study than for the 2008 retrospective  cohort used for our January 2012 report. We attributed these differences  to beneficiaries who were in our 2008 cohort but not our 2010 cohort as  having, on average, greater differences between their actual and  estimated disease scores than those beneficiaries who remained in MA. It  is possible this is due to sicker beneficiaries with greater coding  differences relative to Medicare FFS leaving the study cohort as they  aged, either because of death or to rejoin Medicare FFS. We expect that  beneficiaries who leave MA or die after 2010 would have a similar effect  on our 2011 and 2012 projections. To address this, we adjusted our  estimates of the impact of coding differences on the basis of the  magnitude of differences in the impact of coding differences for our 2008  and 2010 retrospective cohorts during each period. We applied these  adjustments relative to the projection year (i.e., half of the differences  between the 2008 and 2010 cohorts for periods 2005-2010 were applied  to 2006-2011 for our 2011 projection and the full difference between the  cohorts was applied to 2007-2012 for our 2012 projection).
section_title: Estimating the Impact on Payments to MA Plans in 2010, 2011, and 2012
paragraphs: To quantify the impact of both our and CMS’s estimates of coding  differences on payments to MA plans in 2010, 2011, and 2012, we used  data on MA plan bids—plans’ proposed reimbursement rates for the  average beneficiary—which are used to determine payments to MA plans  and information on MA plan enrollment. We analyzed monthly MA  enrollment for 2010 and 2011, and because enrollment data for 2012 was  incomplete, we used the annual trend for these 2 years in conjunction  with actual enrollment for the first 9 months of 2012 to estimate total 2012  MA enrollment. We used these data to calculate total risk-adjusted  payments for each MA plan before and after applying a coding  adjustment, and then used the differences between these payment levels  to estimate the percentage reduction in total projected payments to MA  plans resulting from adjustments for coding differences in 2010, 2011,  and 2012.associated with each adjustment to the estimated total payments to MA  plans—$114.8 billion in 2010, $122.9 billion in 2011, and $133.5 billion in  2012—and accounted for reduced Medicare Part B premium payments   Then we applied the percentage reduction in payments  received by CMS, which offset the reduction in MA payments (see   table 1).
We analyzed data from CMS on Medicare beneficiaries, including data  collected from Medicare providers and MA plans. We assessed the  reliability of the CMS data we used by interviewing officials responsible  for using these data to determine MA payments, reviewing relevant  documentation, and examining the data for obvious errors. We  determined that the data were sufficiently reliable for the purposes of our  study.
section_title: Appendix II: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: In addition to the contact named above, Christine Brudevold, Assistant  Director; Alison Binkowski; William A. Crafton; Gregory Giusto;   Andrew Johnson; Richard Lipinski; Elizabeth Morrison; and   Jennifer Whitworth made key contributions to this report.