S&P Healthcare Claims Indices: Checking the Pulse of the Healthcare Market Table of Contents

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1 S&P Healthcare Claims Indices: Checking the Pulse of the Healthcare Market Table of Contents 1. Introduction 2. S&P Healthcare Claims Indices 3. Index History and Analysis 4. Potential Uses for the Indices 5. Healthcare Reform and the Importance of the Indices 6. Conclusion

2 1. Introduction to the S&P Healthcare Claims Indices In October 2013, S&P Dow Jones Indices (S&P DJI) launched the S&P Healthcare Claims Indices (the indices). This new index series is designed to provide an independent, timely measure of the changes in healthcare expenditures and utilization for individuals enrolled in commercial health insurance plans in the United States. S&P DJI developed these new indices in conjunction with healthcare professionals at Health Index Advisors (HIA), a joint venture between the premier actuarial and consulting firms Aon Inc. and Milliman Inc. S&P DJI combined its knowledge and experience in developing leading indices with HIA s experience in the healthcare market to develop the first index series of its kind, based on actual healthcare claims data. These indices seek to increase transparency in the healthcare market and enable the analysis and tracking of changes in healthcare expenditures. The indices will replace the S&P Economic Healthcare Indices, which were originally launched as the Milliman Health Cost Index in The S&P Economic Healthcare Indices utilized a model-driven approach to measuring the changes in healthcare costs. Over the past three years, the Economic Indices have provided a solid gauge of healthcare cost trends in a space where few other reliable measurements exist. However, unlike the Economic Indices which are model-driven, the new S&P Healthcare Claims Indices are based on actual claim data collected, representing over 60 million individuals (approximately 40% of commercially insured fee-forservice population in the U.S.) from 33 contributing plans 1. In addition, the S&P Healthcare Claims Indices provide pharmaceutical-related information. Total healthcare cost trends 2 have varied widely since the 1970s. Over the most recent decades, trends have varied from annual increases in the midsingle-digits to mid-teens. 3 This variability makes it difficult for employers, health insurance carriers, government organizations and others to effectively manage the financial liabilities associated with healthcare programs. In addition, during this time, the industry lacked a timely, comprehensive and objective benchmark to consistently gauge the change in healthcare costs. The S&P Healthcare Claims Indices are designed to provide a solution to these issues. This paper covers the following topics: An overview of the S&P Healthcare Claims Indices; Index history and analysis; Potential benefits that the indices can provide to healthcare experts; and Healthcare reform and other market activity driving the need for a timely measure of healthcare claim cost increases. Key Contacts: David Blitzer, Ph.D. S&P Dow Jones Indices Managing Director Bo Chung S&P Dow Jones Indices Managing Director Glenn Doody S&P Dow Jones Indices Vice President Like What You Read? Sign up to receive complimentary updates on a broad range of index-related topics and events brought to you by S&P Dow Jones Indices. 1 Data as of September The use of the term trend in this document refers to the rate of change in per capita healthcare allowed claim costs. 3 Pricewaterhouse Coopers Health Research Institute; Behind the numbers* Medical cost trends for 2010, June 2009, page 6, figure 2. 2

3 2. S&P Healthcare Claims Indices 2a. Index Overview The indices measure the changes in healthcare expenditures and utilization for individuals enrolled in commercial health insurance plans in the U.S. The expenditures and utilization measures cover inpatient and outpatient care, as well as generic and branded prescription pharmaceuticals. Several large data providers give S&P DJI the data on which the index calculations are based. These providers cover approximately 40% of the total commercially insured Fee-for-Service ( FFS ) healthcare market in the U.S. 2b. Governance The indices are maintained and governed by the Index Committee. The Index Committee members are drawn from S&P DJI and Health Index Advisors. The majority of the members and the Index Committee chairman are employees of S&P DJI. The Index Committee has complete discretion to determine how the indices are calculated and how the data are validated. S&P DJI considers information about changes to the indices and related matters to be potentially market moving and material. As a result, all Index Committee discussions are confidential. 2c. Index Construction and Methodology The following is an overview of the index construction process and methodology. For additional detail, please refer to the S&P Healthcare Claims Methodology at 2c.i. Index Coverage The indices are intended to provide a basis for measuring the change in the average cost over time of commercial (private) health insurance plans that provide: Comprehensive coverage or reimbursement for medical expenses based on allowed actual charges (the total cost of services including any participant out of pocket expenses); and Coverage to a working age population and dependents. As such, the indices generally do not include the costs associated with various public health insurance programs, even when those programs are provided through private, commercial insurance carriers. This section will provide a short outline of the various types of private and public health insurance programs currently operating in the U.S. and clarify the relationship of each program to the indices. Commercial (private) health insurance programs include health coverage offered both by private health insurance and employers. Private insurance can be classified into three groups: 1. Insured health coverage. Comprehensive health coverage offered under an insurance contract by a private health insurance carrier. Coverage under such insurance programs may be available through an employer-sponsored program (group insurance policies) or may be purchased directly by consumers (individual insurance policies) without any employer involvement. All health insurance programs are regulated at the state level. Most insurance policies are managed care programs, incorporating a network of preferred healthcare providers. Benefit coverage will vary according to whether the provider of services is a member of that network. The indices include data from both group and individual insured health programs. 2. Self-Insured health coverage. A comprehensive form of health coverage typically offered through larger employers and exempt from state insurance regulation through an Employee Retirement Income Security Act ( ERISA ) exemption. Employers will typically engage a health insurance carrier to provide administrative 3

4 services (such as claim payment services, managing preferred provider networks and other services) on behalf of the employer, who is the plan sponsor. As with insured plans, most self-funded employer medical plans are based on a managed care model, with an associated network of healthcare providers and reimbursement of medical expenses based on a negotiated rate. The indices include data from self-insured programs. 3. Limited medical insurance coverage (insured and self-insured). Restricted insurance programs that typically pay a fixed amount for the diagnosis of a specified condition (such as cancer) or due to a specific medical service (such as an inpatient admission or an accidental injury). The benefits under these programs are typically paid based on a schedule of benefits (often as a lump sum payment) and are not directly related to the actual amount of medical expenses incurred by the covered individual. Since benefits under these programs are stated as fixed amounts and do not vary with actual medical costs, limited medical insurance coverage does not reflect the impact of any changes in the actual market cost of covered healthcare services. For these reasons, data from limited medical programs are not included in the indices. Public health insurance programs are statutory programs managed at the federal and state level. These programs are designed to provide health coverage to specific populations. Although the indices do not cover any public health insurance programs, regardless of whether the actual insurance is through the traditional Medicare program or a private insurance program, this paper includes an overview of the various types of programs available in the public sector. Primary public health insurance programs include: Medicare is a federal program that provides health coverage to covered individuals ages 65 and over; totally disabled individuals of any age following a minimum of 29 months of disability; and individuals of any age who have end-stage renal disease. Medicare and related private health insurance programs include: o o o o Traditional Medicare (Parts A and B), a FFS health insurance program administered by the Centers for Medicare and Medicaid Services ( CMS ); Medicare Advantage plans (also referred to as Medicare Part C plans), which are provided through private health insurance programs; Medicare Part D, which provides coverage for prescription drugs through private insurance programs, including Medicare Advantage plans; and Medicare Supplemental plans (also referred to a Medigap plans), which are private insurance plans that cover out of pocket expenses not reimbursed by traditional Medicare coverage. Medicaid is a joint federal and state program (also administered by CMS) generally aimed at providing health insurance to individuals with lower incomes who obtain coverage by means testing (determined by reference to the federal poverty level). State Children s Health Insurance Programs (SCHIP) are joint federal/state programs that provide coverage to children and, in some states, pregnant women. Workers Compensation programs are statutory plans enacted at the state level to provide coverage for employment related injuries and conditions. Other public health programs include the Veterans Health Administration, the Indian Health Service, the Military Health System/TRICARE and the Federal Employees Health Benefits Program. 2c.ii. Data Sources S&P DJI has obtained data representing approximately 40% of the commercially insured FFS population of the U.S. These data are provided by seven organizations representing 33 contributing plans and all 50 states. The smallest geographic unit identified in the data collection is a three-digit zip code. However, no indices are published at the three-digit zip code level. Data are aggregated in the calculation of indices for states, regions, and selected metropolitan areas. 4

5 These data provide specific information on the claims under commercial insurance programs broken into several categories: Type of Measure. Includes cost, utilization and unit cost. Costs are measured in U.S. dollars. Utilization measures vary by the particular healthcare service: o Inpatient hospitalization utilization is measured as the number of days in an inpatient acute care hospital. o Pharmaceutical usage is measured as the number of days supply of prescription medication provided. Lines of Business Coverage. Data are separate for individual insurance policies, small group policies, large group policies and Administrative Services Only ( ASO ). ASO refers to self-insured employer medical programs where the insurance carrier administers the plan while the plan sponsor or employer covers the insured costs. Healthcare Payment Type. Medical plans provided through the above lines of business typically pay healthcare providers on a FFS basis. FFS payments are the traditional method of paying healthcare services. Under FFS, the provider is paid a specific amount for a specific service. Services are typically defined according to a published standard (such as the Current Procedural Terminology manual that describes physician services, defined in the glossary section) or a contractual arrangement between the healthcare provider and the insurance carrier. Capitation is another method of financing healthcare insurance programs by paying a healthcare provider organization a fixed amount per enrolled member each month as full payment for a defined set of healthcare services, regardless of the actual services provided. In essence, capitation establishes a fixed budget payment amount for a provider in place of the FFS system. The appeal of capitation is that it is viewed as a way to align the financial interests of the healthcare provider with the organization that ultimately pays for the cost of health insurance. Since the capitated provider is committed to providing services for a fixed budget, the incentive is for the provider to deliver medical services more efficiently and eliminate unnecessary services. Because capitation is still an emerging trend and is concentrated in certain market segments, not enough data sets are currently available to create a robust, representative index. However, the S&P Healthcare Claims indices currently do include a very specific type of capitation data. In some instances members are covered under partial capitation programs for limited services (such as lab tests or mental health expenses) for FFS enrollees. Values in this category are included in FFS Medical Cost and FFS Total Cost Indices. Healthcare Payment Type (Categories I, II, and III). This includes inpatient facility, outpatient facility, professional services (services provided directly by physicians and other healthcare professionals) and prescription drug expenses (broken down according to brand name drugs and generic drugs). Geographical Region. Indices are based on various geographic regions including census division, census region, state and various metropolitan areas. Data are provided to S&P DJI by the three-digit zip code for each of the covered members residence. Incurred Period. The indices are based on incurred data. Due to the lag time between when a claim is incurred, and when the insurer receives and processes the claim, there can be significant lags in the publication of the indices. S&P DJI publishes a separate series based on incurred data reported after three, six and 12 months. S&P DJI retains a complete history of the initial and revised indices. There is a more in-depth study of incurred lag periods later in this document. More than 31,000 index combinations can be created through various groupings by lines of business, medical, and pharmaceutical, as well as by distinct geographic breakdowns. Due to restrictions in the methodology, which is designed to protect data contributor confidentiality, S&P DJI will not publish all of these combinations. 5

6 All indices are based on the aggregated data accumulated from all contributing organizations in order to maintain the confidentiality of each contributing party s data. Due to confidentiality obligations to each of the contributing organizations, S&P DJI will not create indices based solely on data from an individual health insurance company. In addition, S&P DJI will not publish certain indices if the number of members in a plan or a geographic area does not reach certain critical levels. The indices comply with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) restrictions, which provide federal protections for individually identifiable health information held by covered entities and their business associates. All individual coverage data are aggregated at the three-digit zip code level by the contributing plans before submission to S&P DJI. All zip codes with a population of less than 20,000 individuals are not submitted to S&P DJI. The following diagram represents an overview of the S&P Healthcare Claims Indices. Exhibit 1: Overview of the S&P Healthcare Claims Indices Source: S&P Dow Jones Indices. For a definition of S&P Region, Sub State and S&P Metropolitan Area, please refer to section 3e. 6

7 % Complete S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October c.iii. Index Calculation Index calculation of the monthly change in healthcare costs occurs in three steps: 1. The cost variable is divided by the completion factor (described later in the paper) to scale it up to the estimated 12-month completed claims figure (with the actual 12-month incurred claims data available in the 12 th monthly update to the incurred series). 2. The adjusted cost figure is divided by enrollment to calculate the Per Member Per Month (PMPM) measure of the cost. 3. The proportional change from the previous to the current month in PMPM is calculated and applied to the previous month s index level to calculate the current month s index level. 2c.iv. Completion Factors As noted in the index calculation discussion above, completion factors are used to adjust incurred series variables based on less than 12 months of updated data. This section discusses the estimation of these factors. Payments made for a given incurral month are reported over 12 consecutive months as the data become available to the insurance plans and claims are processed. Because the reporting patterns are relatively stable, a schedule showing the percentage of complete data available for each month after incurral can be calculated and used to estimate the complete or total number before the complete 12 months of data are available. The schedule can be illustrated as a curve of monthly completion factors stated as percentages of the final total. The shape of the completion curve depends on how quickly claims are processed after healthcare services are rendered. The processing time is likely to vary depending on insurance carrier, line of business, utilization and cost data, as well as specific services such as inpatient versus outpatient office visits. To isolate these variables, the effects of time and geography are removed through an averaging process. At the most granular level, incurred values are reported for a service type, line of business, zip code, and period date over a 12-month period. The final 12 th month incurred value is assumed to be 100% complete since no further values are reported for the given period date. Accordingly, the incurred values are divided at this level by its 12 th month value for a given period date (see Exhibit 2). Exhibit 2: Sample Incurred Values for Period Date 120% 100% 80% 60% 40% 20% 0% Mar-13 Apr-13 May Months Out Source: S&P Dow Jones Indices. An average is taken over all curves within a 36-month window. The averaged curve will be specific to a service type, line of business and zip code (see Exhibit 3). 7

8 % Complete % Complete S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October 2013 Exhibit 3: Sample Average Incurred Values Across a 36-Month Window 120% 100% 80% 60% 40% 20% 0% Mar-13 Apr-13 May-13 Average Months Out Source: S&P Dow Jones Indices. A final average is taken over all zip codes, weighted by their average enrollment over the 36-month window. Based on reviews of test data, completion factors for low enrollment (less than 1,000 plan members) zip codes tend to be volatile and unstable. The weighting scheme reduces volatility in zip codes with lower enrollment. The 12 values in the final curve, dubbed completion factors, are applied to incurred values for that specific service type and line of business. These completion factors are independent of geography and used to adjust incurred PMPM series at any geographic level (See Exhibit 4. Exhibit 4: Sample Completion Curve for (Service Type, Business Type) 120% 100% 80% 60% 40% Average -2 Standard Deviations +2 Standard Deviations 20% 0% Months Out Source: S&P Dow Jones Indices. In addition to calculating completion curves, the data are also used to calculate the standard deviation of each completion factor. This provides an approximate confidence interval of +/- two standard deviations around the curve and gives an indication of the reliability of the index levels based on incurred series with less than 12 months of revisions. Exhibit 4 shows a typical curve with the confidence interval. The curves can be compared using the approximate confidence interval calculated for each completion curve. The curves are considered to be similar where they lie within the confidence intervals of each other. A default curve is used for substitution purposes if any completion curves are missing data or contain extreme values. The default curve is calculated as an average over all plans. Completion curves are recalculated and reviewed annually. 2c.v. Data Capping The indices are always based on data from multiple plans. If a single plan represents a substantial portion of the data supporting a particular index, two potential outcomes can occur: Either the index calculation is adjusted to cap the impact of data from that plan on the overall index, or that index is not published. The capping process adjusts the proportion of total enrollment to limit the impact of data from a single plan on the index. Capping limits have been established to ensure that the redistribution of weights from one plan to another does not materially distort the index. 8

9 For non-pharmaceutical indices, a minimum enrollment of 7,500 members is required for publication. For pharmaceutical indices, a minimum enrollment of 5,000 members is required. However, despite these minimum enrollment limits, significant trend fluctuations can still occur due to catastrophic claims and other contributing factors. 2c.vi. Restatements Incurred data are revised each month until 11 monthly updates are available for a particular incurred series data point. S&P DJI publishes a separate series based on incurred data reported after three, six and 12 months. This series of three indices is calculated using incurred data. Preliminary Indices: The first, or preliminary series (three-month), uses data initially reported (onemonth incurred) and then updated twice over the next two months (two- and three-month incurred). Intermediate Indices: The second series (six-month) uses data initially reported and updated over five subsequent months (incurred data for months one through five). Completion factors are applied to these data to estimate the levels for the full 12 months of reporting. These estimates are expected to be stable based on our review and examination of the completion factors. This allows for publication of indices approximately three and a half months after month-end. Final, Completed Indices: The third or final series is based on completed incurred data the initial report plus 11 monthly revisions to the incurred data series. S&P DJI retains a complete history of all indices calculated. 2c.vii. Monthly Data Validation Validation tests are conducted at the lowest level of aggregation for which an index is published. The overall process consists of the following three steps: 1. Database Checks. Routine data loading procedures are applied to confirm that these data are in correct fields, include correct identifiers and that file sizes are as expected. Missing or incorrect data are excluded and the data provider is subsequently contacted for corrected data. 2. Actuarial Comparisons. Selected variables and data types are compared to actuarial and industry standard ranges to check data classification. 3. Statistical Review of Monthly Data. Using statistical tests, the occurrence of extreme values is tabulated for each contributing plan in each month of the development data set. If the occurrence of extreme values in the current month does not exceed the largest occurrence reported in the development data, the current month data are accepted. If it does exceed, the matter is reviewed by the Index Committee to identify the source of the extreme values. If the plan states the data are correct, the indices are calculated and published. If new data are submitted the validation process is repeated for the new data. In all cases, the Index Committee may decide not to publish certain indices if it believes that these data are not correct and reliable. 2c.viii. Sample Size Validation: Making a Case For Representation The indices depend on both the quality of data that S&P DJI collects from contributing health plans (the database), as well as sampling methodology to ensure that the indices are representative. While S&P DJI makes no representation that any one index is accurate, or that the sampling size is sufficiently comprehensive, we ran various tests against the sample sizes. These tests indicate that the sample sizes used in the development of the indices are representative with a low margin of error. The indices are currently compiled from the database accounting for approximately 60 million lives out of the roughly 148 million commercially insured FFS lives covered in the U.S. This represents about 40% coverage at the U.S. national level for all lines of business. In order to determine whether the incurred claim cost data in a geographic area in the database is credible relative to the total potential incurred claim cost for the area, a sampling methodology was implemented based upon the enrolled members in the database for that area. The factors used to derive the membership 9

10 size include: Level of confidence; Variance estimate; and Desired precision. In most cases, the analysis was based on the assumption that the incurred claim cost be measured within a ±1% margin of error at a 99% confidence level. Variance estimates by benefit type were determined using independent models (i.e., not dependent on the database), assuming individuals are fully complete, identically distributed by age, gender, geographic area, health status and so forth. While this may not work for all U.S. states (for example, Alaska has a distinct population ratio of males versus females), S&P DJI believes it does provide a solid basis for the study. The analysis also included the assumption that individual incurred claims are randomly distributed and those not in the database are not materially different from those that are compiled in the database. In support of this assumption, S&P DJI conducted a correlation study, which indicated that the cost data provided by various contributors is highly correlated, with correlations among data contributors ranging from 0.79 to For the purpose of this study, S&P DJI tested the four lines of business (ASO, large group, small group and individual), as well as each of the healthcare services (healthcare, medical, drug, drug-brand, drug-generic, professional services, facility, inpatient and outpatient). Moreover, S&P DJI tested the data at the national level, census division level and census region level, and examined a mix of large and small states in the U.S.- -New York, Florida, Texas, New Mexico, Oklahoma and Washington State. Finally, S&P DJI conducted indepth research on three metropolitan areas, Dallas, Chicago and Seattle. At the state and metropolitan levels, S&P DJI is confident that the data are representative. However, S&P DJI expects that the margin of error for the state and metropolitan levels will be somewhat higher when compared to the broader census division, census region and national levels. Statistical tests indicate that there is 99% confidence that the indices have enough data at the national level across all lines of business within a 1% margin of error (see Exhibit A-1 in the Appendix). At the census region, division and state levels, the confidence level in the data remains high across all lines of business (99% confident within a 1% margin of error). The only exceptions were at the state level, with New Mexico and Oklahoma, where the margin of error increased slightly. Throughout this study, the margin of error across various lines of business and at the lower geographical breakdowns increases primarily due to the decrease in both overall population and sample size, especially in the smaller regions. However, most geographical breakdowns tested indicate fairly high confidence levels in the data. Testing indicates higher margins of error in the inpatient services. This is primarily due to catastrophic claims, which in turn increases the volatility in the inpatient cost data. Due to the higher volatility of inpatient cost data, the inpatient data are not as easily predictable with smaller and sometimes average sample sizes. The test results shown in exhibit A-1 in the appendix are based on data through January 2013, with additional tests conducted on the 2008 data indicating similar results. Exhibits 5 and 6 demonstrate the potential effect of various margins of error on claims costs (the underlying data that serve as the basis for the indices). 10

11 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 PMPM Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 PMPM S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October 2013 Exhibit 5: S&P Healthcare Claims 12-Month National All Business Fee-For-Service Medical PMPM: 99% confidence level; 1% margin of error PMPM - Actual +1% Margin of error on Cost -1% Margin of error on cost Source: S&P Dow Jones Indices. Data from March 2008 to March Past performance is not an indication of future results. Chart is provided for illustrative purposes only. Exhibit 6: S&P Healthcare Claims 12-Month New Mexico All Business Fee-For-Service Inpatient PMPM: 99% confidence level; 3.9% margin of error PMPM - Actual +3.9% Margin of error on Cost -3.9% Margin of error on cost Source: S&P Dow Jones Indices. Data from March 2008 to March Past performance is not an indication of future results. Chart is provided for illustrative purposes only. As mentioned previously, S&P DJI also evaluated the margin of error based on the type of coverage (ASO, large group, small group and individual coverage). The experience at the ASO level is similar to the experience at all lines of business at the national and census region levels (see Exhibit A-2 in the Appendix). However, at the census division level, one can begin to see an increase in the margin of error for some of the regions for inpatient claims. At the state level, the margin of error increases across all healthcare services, particularly in the relatively smaller population states of New Mexico, Oklahoma and Washington. With the exception of New Mexico and Oklahoma, the margins of error are still low and indicate a high level of confidence that the indices represent these regions sufficiently. For New Mexico and Oklahoma, S&P DJI reran the tests at the 95% confidence level, which resulted in a drop in the margin of error for facility (hospitals and outpatient care centers) to 6.6% from 8.7% and for total healthcare in the state to 4.4% from 5.8%. At the large and small group levels one can start to see a slightly higher margin of error at both the census division and census region levels (See Exhibits A-3 and A-4 in the appendix). This is particularly notable in the Mountain Region, which had 5.0% margin of error at the 99% confidence level for inpatient services. In cases where the margin of error was higher, S&P DJI tested the data at the 95% confidence level, which resulted in lower margins of error. This margin of error fell to 3.9% when testing at a 95% confidence level. 11

12 Small group and individual margins of error are higher at the small group and especially the individual levels (see Exhibit A-5 in the Appendix). This stems from smaller and more fragmented data samples used for the indices. The margin of error and confidence level have a direct relationship to each other. Possible margins of error can be calculated at any confidence level. For areas with unsatisfactory results, the margin of error can be reduced by combining additional regions or lines of business. 2d. Rationale for Using Incurred Claims Payment of FFS healthcare expenses under commercial insurance coverage can often take place months after the services were actually delivered. The time between the delivery of services and the payment for those services is referred to as claims lag. As a result, the health insurance industry often identifies monthly data as being determined either on a paid claims basis or an incurred claims basis. Details of which are as follows: Monthly paid claims refer to all claims adjudicated and released for payment during a specific month, regardless of the month in which those claims were incurred. For example, all claims that completed processing during the month of March would be referred to as the March paid claims. Monthly incurred claims refer to all claims incurred during a month, regardless of when those claims were actually adjudicated and paid. For example, all claims that were incurred in March (i.e., the services were actually performed in March) would be referred to as the March incurred claims. Typically, there could be a substantial time lag between the date the services were incurred and the date on which the claim for those services was paid. The pattern of the claims data is often referred to as the claim run-out since the incurred claims are not known until several months have run out. Sometimes, the final incurred claims amount for a specific month is not fully complete until more than 12 months after the month the claim was incurred. In order to make the indices as accurate as possible for tracking the changes in healthcare costs over time, the indices are based on incurred claims, or the assignment of all costs to the month in which the services were incurred, not the month when payment was made. As part of the evaluation of the claims data available from the participating organizations, S&P DJI conducted several tests using both paid claims and incurred claims data before deciding how to construct the indices. The key considerations of using each type of data include: Monthly paid data have the advantage of being the most current available information and presents the least amount of processing challenge. For example, the total March paid claims data would be known by mid-april. The paid claims data could then be sent to S&P DJI and would be included in the index update released by S&P DJI in May. In addition, from an index user perspective, the definition of paid claims is easily understandable and readily ties back to the health plan financial statements. The disadvantage of using paid claims is that they are a less accurate measure of actual medical cost trends and are more volatile due to monthly variations in the claim payment processing. Incurred claims data represent a more theoretically correct basis for calculating trends, since it should be the most accurate measurement of underlying changes in utilization and cost. However, incurred claim amounts require at least several months of claim run-out in order to be credible, which means that there would be a delay in the updating of indices. In addition, building an index on incurred claims would require monthly revisions to the most recent monthly index values, as the claim run-out emerges. For practical purposes, using incurred claims would require that S&P DJI establish a rule as to when a month s incurred claim data are sufficiently known to allow these data to be included in the index (i.e., S&P DJI could adopt a rule requiring that three months of claims run-out be available before a month could be 12

13 added to the index. In this case, the January incurred claims measure would not be added to the index until after the March data were available. Once the March data were known, there would be three months of data available to estimate the January incurred claims). After testing the data, S&P DJI was able to determine that using three months of incurred claims run-out was sufficient to allow an estimate of the full incurred claim amount for the month. At the conclusion of the testing, S&P DJI made the decision to use incurred claims data as the basis of the indices. The decision was also made to publish three distinct indices for each month: 1. Preliminary Indices: The three-month indices are based on three months of claims data, to which a claims completion factor (described in a previous section) is applied in order to estimate the full incurred claims amount for the month. Once published, this series is not adjusted to reflect any additional months data. 2. Intermediate Indices: The six-month indices are based on six months of claims data, to which a claims completion factor (described in a previous section) is applied in order to estimate the full incurred claims amount for the month. Once published, this series is not adjusted to reflect any additional months data. 3. Completed/Final Indices: The 12-month indices are based on the full 12 months of incurred data. For those months where 12 months incurred data are not available (i.e., incurred months three through 11), a claims completion factor is applied to estimate the full incurred claims amount. S&P DJI will continue to update each month s data as it becomes available until a complete 12 months of actual data are available. Once 12 months of data has been received, the monthly index value will be considered final, and no additional updates will be made. Exhibit 7: Illustrative Publication Schedule for the Three-, Six-, and 12-month Indices. S&P DJI Publication Incurred Claims Month Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan = First publication month for the 3 month incurred = First publication month for the 6 month incurred = First publication month for the final 12 month incurred Source: S&P Dow Jones Indices. An annual schedule of index release dates is published on each December for the following year. 13

14 2e. Geographical Breakdowns The indices are broken down geographically into census divisions, census regions, states and metropolitan areas. All claims data provided to S&P DJI are aggregated at the three-digit zip code level. To comply with patient privacy HIPAA restrictions, any three-digit zip codes with a total population of less than 20,000 people are removed before the data file is sent to S&P DJI. The zip code data provides S&P DJI with significant flexibility to segregate indices into geographic regions that reflect the U.S. healthcare market variations. 2e.i. U.S. Census Breakdown The U.S. census mapping was used because it allows the country to be broken into four standard census divisions across the continental U.S., and then further broken into nine census regions. Exhibit 8: U.S. Census Breakdown Source: U.S. Census Bureau. These regional breakdowns will allow the viewing of healthcare costs from a broader perspective than state level indices. It will also allow S&P DJI to publish geographical indices including states that S&P DJI would not otherwise be able to publish individually because of data sampling and confidentiality issues at the single state level. In addition to publishing the four census divisions and the nine census regions, S&P DJI may also publish indices for a single state. For select large states where a distinct healthcare markets exists, S&P DJI may publish separate indices for substates (intrastate regions). For example, the state of California will be further broken into Northern California and Southern California markets, better representing the differences in the healthcare market within that state. This also applies to Pennsylvania, which is further broken into Eastern Pennsylvania and Western Pennsylvania. In addition, the South Atlantic census region is further broken down into Upper South Atlantic and Lower South Atlantic, known as S&P Regions. 14

15 At this point in time, indices are only published for the continental U.S. and Hawaii. Based on the lack of available data, there are no indices published for U.S. territories such as Guam or Puerto Rico. This may change in the future if S&P DJI is able to secure healthcare data for these territories. 2.e.ii. Major Metropolitan Breakdown S&P DJI will also offer up to 49 different major metropolitan areas, determined as having populations of greater than 1 million residents in the associated Metropolitan Statistical Areas ( MSA ) based on the 2009 census data. These metropolitan areas as defined by S&P DJI are known as S&P Metropolitan Areas ( SPHR ). The borders of these metropolitan areas are then adjusted to cover the hospital referral regions as determined by the Dartmouth Atlas of Healthcare, which can be found at The Dartmouth mapping is established by determining the location of the patient s residence across all hospital medical discharges. The resulting patterns of referral of patients to tertiary care hospitals establishes the Hospital Referral Regions ( HRRs ). By incorporating this measure of how healthcare services are delivered, the S&P DJI metropolitan area borders are often different than the legal or MSA border of the city. The S&P DJI data boundaries are a better representation for the delivery of healthcare services for that metropolitan area. Often there are several HRR s within a metropolitan boundary, and in such cases S&P DJI may combine two or more HRR s to make up a SPHR. Using the Dallas Fort Worth metropolitan area as an example, two HRRs, Dallas HRR and Fort Worth HRR, have been combined resulting in the Dallas Fort Worth HRR (see Exhibit 9). Exhibit 9: Dartmouth Atlas Healthcare Referral Region (Dallas Forth Worth). Source: Dartmouth Atlas of Healthcare. The next step in the process is to convert these HRRs, as outlined in exhibit 10 to reflect as accurately as possible into the three-digit zip code representation of this map. 15

16 Exhibit 10: Dallas Fort Worth 3-digit zip code conversion SPHR. Source: S&P Dow Jones Indices. The three-digit zip codes selected for the map to best represent the HRR now make up a new healthcare region called the S&P Healthcare region ( SPHR ). While the SPHR is similar to the HRR, there are two key differences: 1. The HRR comprises boundaries that represent a single healthcare region as defined by the Dartmouth Atlas, whereas the SPHR may combine one or more of the HRR regions to make up a metropolitan area. 2. Because the HRR regions do not consist of three-digit zip codes, the SPHR boundary borders may change to reflect the closest possible representation of a HRR. In some cases, the changes that are needed to include key areas, yet still represent a three-digit zip code can be significant (see Exhibit 11 for an example of this). 16

17 Exhibit 11: Los Angeles Three-Digit Zip Code Conversion SPHR. Source: Dartmouth Atlas of Healthcare Conversion to SPHR Source: S&P Dow Jones Indices In many cases metropolitan SPHR regions are a part of a single state, census region or census division. However, this is not always the case. For example, the Dallas Forth Worth metropolitan SPHR extends across the Texas border into Oklahoma making up two states. The same is the case for the Pittsburgh metropolitan SPHR, which crosses three states, three census regions and three census divisions. Exhibit 12 provides a list of all the potential S&P metropolitan SPHRs that may be published and their associated primary census breakdown, as well as any secondary regions into which their borders may cross. Not all S&P metropolitan SPHRs may be published due to limitations on the data and coverage. 17

18 Exhibit 12: Overview of S&P Metropolitan SPHR's. Metropolitan SPHR Atlanta State Census Region Census Division Primary Secondary Secondary Secondary Primary Secondary Secondary Primary Georgia North Carolina South Atlantic Austin Texas West South Central South Birmingham Alabama East South Central South Boston Massachusetts New England Northeast Buffalo New York Middle Atlantic Northeast Charlotte North Carolina South Carolina South Atlantic Chicago Illinois East North Central Midwest Cincinnati Ohio Indiana East North Central Midwest Cleveland Ohio East North Central Midwest Columbus Ohio East North Central Midwest Dallas Fort Worth Texas Oklahoma West South Central South Denver Colorado Kansas Nebraska Mountain West North Central West Midwest Detroit Michigan East North Central Midwest Hartford Connecticut New England Northeast Houston Texas West South Central South Indianapolis Indiana East North Central Midwest Jacksonville Florida Georgia South Atlantic South Kansas City Missouri Kansas West North Central Midwest Knoxville Tennessee East South Central South Las Vegas Nevada Mountain West Los Angeles California Pacific West Louisville Kentucky Indianapolis East South Central East North Central South Midwest Memphis Tennessee Arkansas Mississippi East South Central West South Central South Miami Florida South Atlantic South Milwaukee Wisconsin East North Central Midwest Minneapolis Minnesota Wisconsin West North Central East North Central Midwest Nashville Tennessee Kentucky East South Central South New Orleans Louisiana West South Central South New York City New York New Jersey Connecticut Middle Atlantic New England Northeast Norfolk Virginia South Carolina South Atlantic Oklahoma City Oklahoma West South Central South Orlando Florida South Atlantic South Philadelphia Pennsylvania New Jersey Middle Atlantic Northeast Phoenix Arizona Mountain West Pittsburgh Pennsylvania Ohio West Virginia South South South Seconda ry Secondary Middle Atlantic East North Central South Atlantic Northeast Midwest South Portland Oregon Washington Pacific West Raleigh / Durham North Carolina South Atlantic South Richmond Virginia South Atlantic South Rochester New York Middle Atlantic Northeast Sacramento California Pacific West Salt Lake City Utah Nevada Idaho Wyoming Mountain West San Antonio Texas West South Central South San Diego California Pacific West San Jose California Pacific West Seattle Washington Pacific West St. Louis Missouri Illinois West North Central East North Central Midwest Tampa / St.Petersburg / Clearwater Florida South Atlantic South Tucson Arizona Mountain West Washington D.C. / Baltimore D.C./ Maryland Virginia Maryland South Atlantic South Source: S&P Dow Jones Indices. 3. Index History and Analysis 3a. A Look at the Index History There are more than 31,000 potential indices covering multiple areas of critical healthcare cost components. Healthcare cost trends can be identified from analysis of the monthly pro-forma data for the S&P Healthcare Claims Indices with history that dates back to March This section attempts to focus on just a few illustrative examples of how the indices can be utilized to reveal meaningful insights into the healthcare market. 18

19 % Change Year-Over-Year % Change Year-over-Year S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October 2013 A good place to start the analysis is a review of overall healthcare costs for the U.S., which includes both medical and drug coverage, as illustrated by the S&P Healthcare Claims National Indices. Exhibit 13 shows the three-, six- and 12-month incurred index levels. The period illustrated in the exhibit covers the introduction of the Affordable Healthcare Act ( ACA ). A significant decline in the rate of increase in healthcare costs from February 2010 through around February 2011 can be observed, with the growth in healthcare claims at a high between 8% and 9% growth year-over-year ( YoY ) at the beginning of the period, to a low of between 3% and 4% YoY growth around May These indices can be used as a tool to identify cost trends in the healthcare market and could prompt further analysis into the potential factors driving the trends. For example, while healthcare claims have been trending downward, what is the exact effect of implementation of the ACA on healthcare costs? And how much is attributable to the general recovery in the U.S. economy following one of the worst recessions experienced in decades? Further analysis of the indices and various subindices focused on specific segments of the market could result in meaningful conclusions. Exhibit 13: S&P Healthcare Claims National All Lines of Business Fee-For-Service Total Cost Index 9.0% 12 Month Completed 6 Month Incurred 3 Month Incurred 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. While Exhibit 13 offers good insight into the decline in the rate of increase in healthcare costs, the next step is to identify the drivers behind this decline. By looking at the S&P Healthcare Claims Indices, national medical and drug/rx indices, one can see the change in YoY cost for two key healthcare categories (see Exhibit 14). Exhibit 14: S&P Healthcare Claims Indices: National Medical versus Drug/Rx 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% -2.0% Medical Drug Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. 19

20 % Change Year-Over-Year % Change Year-Over-Year S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October 2013 Exhibit 14 clearly shows that the decline in the rate of change of healthcare costs occurs in both medical and drug/rx. However, the graph also shows that the rate of increase in healthcare costs was significantly higher for medical, and remains consistently trending above drug YoY costs. It is also interesting to note that during 2011, the rate of increase for medical stopped declining and started to slowly rise again, while the rate of increase for drug costs continued to decline until late 2011 and sharply increased in early Further analysis of the indices measuring the change in cost for drugs reveals additonal insight into the sources of the increase. The significant increase in the rate of change of cost for drugs in 2012 can be attributed to generic drugs (see Exhibits 15 and 16). This, however, is mainly driven by an increase in the rate of change of utilization for generic drugs, versus brand drugs, which showed a steady decline in rate of change in utilization. The net result was a higher and steady rise in unit costs for brand name drugs versus generic. In large measure, the change in brand and generic utilization is driven by the actions of health plans and self insured (ASO) employers seeking to drive down the overall cost of their prescription drug benefits. Exhibit 15: S&P Healthcare Claims Indices: Brand versus Generic Drug/Rx Cost 20.0% 15.0% 10.0% 5.0% 0.0% -5.0% Brand Cost Generic Cost Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. Exhibit 16: S&P Healthcare Claims Indices: Brand versus Generic Drug/Rx Utilization Comparison 15.0% 10.0% 5.0% 0.0% -5.0% -10.0% -15.0% -20.0% Brand Utilization Generic Utilization Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. One can analyze the results further by looking at the rate of change in healthcare costs for professional services and hospital services. As evidenced by the indices in exhibit 17, both hospital and professional services experienced a decline in the rate of change of healthcare costs. Further analysis of additional detail (not included in this document) indicates that the experience at the inpatient and outpatient levels is very similar to overall trends, with both segments experiencing a decline in the rate of change of healthcare costs during 2010, and a slow increase throughout 2011 and One reason that the actual rate of increase for hospital costs increased more rapidly than for professional services may be that in most metropolitan 20

21 % Change Year-Over-Year % Change Year-Over-Year S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October 2013 markets, large hospital systems have a stronger negotiating position with health plans than do physicians. Exhibit 17: S&P Healthcare Claims Indices: National Hospital versus Professional 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Hospital Professional Services Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. To understand how the rate of change in healthcare costs affected various regions in the U.S., one could analyze the geographic indices. For the purpose of this study, four states were sampled from all available geographic segments, which include the 50 states and the District of Columbia, as well as the four census divisions and nine census regions. In addition, S&P DJI also has coverage for 49 metropolitan areas that can be used for similar studies. Each of the geographical regions are broken down in the same way and at the same level of detail as the national indices above: medical/drug, hospital /professional services and inpatient/outpatient. Texas, Florida, New York and Illinois were the four states chosen to illustrate changes in regional healthcare costs. These states were selected because they represent different areas of the country, and very diverse demographics. In all cases, each state experienced the same decline in the rate of change of healthcare costs througout 2010 as at the national level (see Exhibit 18). However, the significant difference occurs in late 2012/early 2013 when the rate of change of healthcare costs for New York and Illinois decreased significantly, while remaining relatively flat for Florida and Texas. While the trends for the four states have the same general shape, the graph shows that the actual rate of healthcare cost increases at any time can be significantly different by state. Exhibit 18: S&P Healthcare Claims Indices: State Trends in Healthcare 12.0% 10.0% Illinois New York Florida Texas 8.0% 6.0% 4.0% 2.0% 0.0% Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. 21

22 % Change Year-Over-Year % Change Year-Over-Year S&P HEALTHCARE CLAIMS INDICES WHITEPAPER October b. Comparing the Indices to Healthcare Employment and Wages We also conducted a study that compared earnings and employment in the healthcare sector with the S&P Healthcare Claims Indices. As part of this analysis, S&P DJI identified a lag effect between one-year employment and earnings. As evidenced below, there is a definite correlation between the rate of change in inpatient utilization as measured by the S&P Healthcare Inpatient Index, and hospital employment. Although hospital employment did not dip as much as the index trend, it clearly declined. In addition, as the rate of change in utilization increased, the rate of change in hospital employment expanded. The same can be seen below with hospital earnings (see Exhibit 19). Again, hospital earnings are lagged by a period of one year. As the rate of change in costs associated with hospital claims declines, the rate of change of hospital earnings also drops. The inverse is also true, with very little difference in volatility between the two trend series. Exhibit 19: Lagged Hospital Earnings versus S&P Healthcare Claims 3-Month National All Lines of Business Fee-For-Service Facilty Total Cost Index 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13 Jul Month Lag Hospital earnings per capita S&P Healthcare Claims 3-Month National All Lines of Business Fee-For-Service Facility Total Cost Index Source: S&P Dow Jones Indices and the Bureau of Labor Statistics. Data as of July Past performance is not an indication of future results. Chart is provided for illustrative purposes only. Exhibit 20: Lagged Physician Earnings versus S&P Healthcare Claims 3-Month National All Lines of Business Fee-For-Service Professional Services Total Cost Index 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13 Jul Month Lag Physician earnings per capita S&P Healthcare Claims 3-Month National All Lines of Business Fee-For-Service Professional Services Total Cost Index Source: S&P Dow Jones Indices and the Bureau of Labor Statistics. Data as of July Past performance is not an indication of future results. Chart is provided for illustrative purposes only. Physician earnings, on the other hand, are not as stable (see Exhibit 20) and exhibit a greater correlation to declines in the rate of change in the S&P Professional Services Index. 22

23 3c. Interest Rate Sensitivity It should come as no surprise that healthcare costs are sensitive to interest rates. For the purpose of this study we have compared the change in heathcare costs to the S&P/BGCantor 0-3 Month U.S. Treasury Bill, the S&P/BGCantor 1-3 Year US Treasury Bond Index and the S&P/BGCantor 3-5 Year US Treasury Bond Index. There is much more sensitivity at the shorter end of the interest rate curve than at the longer end. Exhibit 21: S&P National Healthcare Claims Index versus S&P/BGCantor 0-3 Month U.S. Treasury Bills 0.90% 0.80% 0.70% 0.60% 0.50% 0.40% 0.30% 0.20% 0.10% 0.00% S&P/BGCantor 0-3 Month US Treasury Bill (TR) S&P National Healthcare Index 9.0% 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. Exhibit 22: S&P National Healthcare Claims Index versus S&P/BGCantor 1-3 Year US Treasury Bonds 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% S&P/BGCantor 1-3 Year US Treasury Bond (TR) S&P National Healthcare Index 9.0% 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. As illustrated above, when short-term rates decline, there is almost an immediate effect on on healthcare costs. However, when examining healthcare costs relative to the longer end of the interest rate curve the rate of change in healthcare costs starts to decline much quicker than the decline in interest rates (see Exhibits 22 and 23). Empirical evidence shows that there is a strong correlation between T-Bills and inflation. As inflation declines, T-Bill yields drop and vice versa. If inflation rises, by definition, the cost of goods and services rise. Given that healthcare costs are a basic service that is instrumental to everyday life, it is not surprising that the rate of change in healthcare costs are tied closely to changes in interest rates. It is worth noting that there is no lag in movement between short-term rates and the rate of change in healthcare costs. This could indicate that healthcare claims are very sensitive to inflationary changes. 23

24 Exhibit 23: S&P National Healthcare Claims Index versus S&P/BGCantor 3-5 Year US Treasury Bonds 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% S&P/BGCantor 3-5 Year US Treasury Bond (TR) S&P National Healthcare Index 9.0% 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% Source: S&P Dow Jones Indices. Data from February 2010 to April Past performance is not an indication of future results. Chart is provided for illustrative purposes only. 4. Potential Uses for the S&P Healthcare Claims Indices The S&P Healthcare Claims Indices are unique because they are the first index series to comprise healthcare data from approximately 60 million commercially covered lives. As such, S&P DJI expects that the indices should be a valuable tool to health plans, healthcare provider systems, employers and other parties. The analysis resulting from these indices could result in better risk management and analytic tools for the broader healthcare industry. 4a. Potential Uses for Health Insurance and Reinsurance Providers Since the indices represent a benchmark trend for healthcare costs, they can be used by insurers to analyze past experience trends and thus could help to set future rates. Potential uses include: Guaranteeing costs for their corporate ASO clients Despite the fact that a health plan may negotiate the best available payment rates (which limits price risk) on behalf of a particular ASO employer, the employer is still exposed to the total cost (which includes utilization risk) of healthcare services for the term of the contract. By utilizing the indices as a basis for providing total cost guarantees, the health carrier can provide a complete guarantee to an ASO-based employer. Moreover, the indices can be applied for periods in excess of 12 months, leading to long-term contracts locking in a total cost ceiling for the employer. This type of financial guarantee could be very valuable to employers. Controlling volatility of costs with health service providers Health service providers and health plans negotiate their rates, which are locked in for the duration of the contract. These rates are important because they reflect costs to the health plans and revenues for the service providers. Over a longer term, plans and service providers could explore ways to utilize the index to smooth the volatility in revenues and costs. For example, health insurance carriers and healthcare provider organizations can use the indices as a tool to establish risk-based contracts. These contracts should move financial risk for the insurer away from the FFS model toward a shared risk outcome with the healthcare provider. This aligns the financial incentives between payer and provider and rewards efficiency. The indices should be particularly valuable to the development of risk-based financing methods for the new Accountable Care Organizations (ACOs) established under the healthcare reform legislation. 24

25 Reinsurance Reinsurance in the healthcare sector is limited, in part, due to a lack of common historical benchmarks to track healthcare costs. Plans and reinsurers could apply these indices to provide a greater range of reinsurance coverage, using the indices to settle the contracts. 4b. Potential Uses for Health Consulting Firms Healthcare consulting firms of all types, including employee benefit consultants, healthcare system consultants and financial consulting firms, could use the indices to understand where specific trends in healthcare costs are originating, and how to respond. 4c. Potential Uses for Investment Banks Investment banks could utilize the indices in two ways: 1. Data for analysis These data would enhance their ability to compare their covered companies to industry averages and help improve their analyses. Investment banking operations would also be able to utilize the data when evaluating the issuance of new stock or debt for a public health plan or medical service provider. 2. As the basis for the creation of products Trading desks can utilize the indices for creation of products related to healthcare hedges. This could offer health insurers a means to offset risk on their cost guarantee products, and the banks could then further offset that risk to various capital market participants such as hedge funds. The same could apply to catastrophic hedging solutions, with the banks taking on the risk, and offsetting this risk into catastrophic bond issuances. 4d. Potential Uses for Large Hospital and Healthcare Systems Unlike the health plans, which have very sophisticated capital markets desks, most healthcare service providers have never participated in capital market activity other than for the issuance of bonds to finance their capital growth. Potential uses include: Budgeting Purposes Medical service providers can use these data for negotiation purposes with the plans, as well as for budgeting purposes. Medical providers can also utilize indices to help guide forecasts for future revenue growth or to control their revenue volatility making revenue streams more predictable. Competitive Positioning Assessment Healthcare service providers can also utilize the data for comparing their competitive positions in the local markets. 4e. Potential Uses for Employers with ASO (Administrative Service Only) Plans Employers may be able to use the indices for: Research and Benefits Analysis Instead of relying on industry consultants to provide the research and on third parties such as health plans to offer rate guarantees, the employers internal benefits group could perform analysis on healthcare claims index data. Setting Healthcare Expenditure Rates 25

26 Some industry clients may also use the indices to set healthcare expenditure rates in their union legal agreements. 4f. Potential Uses for Government Agencies Public policy makers and regulators could use the indices to measure the impact of enacted legislation/regulation and anticipate the future implications of potential policy and regulation. Public policy makers will be able to use these indices to gain insight into the current rates of cost increases nationally and regionally. In addition, by using the vast amount of detailed trend data available through the indices, they can analyze the differences in trends and enrollment between the various lines of commercial coverage and establish a more accurate understanding of the impact of the healthcare reform legislation. Regulators, such as state insurance departments, can use the indices to evaluate the relationship between health insurance premium trends and the underlying change in the cost of healthcare services for insured populations. In most cases, the state insurance department has responsibility of maintaining the solvency of insurance carriers, as well as providing oversight to keep premium costs as affordable as practical. The indices can provide a valuable basis for balancing these two objectives. 5. Healthcare Reform and the Importance of the Indices In an environment of increased awareness and regulation of the healthcare industry, it is important to understand and monitor current changes in healthcare costs. Such information can help reduce uncertainty for the general public, insurers, employers and other market participants. It will also help private and government organizations better understand the primary components driving increases in healthcare costs. The S&P Healthcare Claims Indices provide an objective, timely, transparent and robust measure of healthcare cost trends. 5a. Patient Protection and Affordable Care Act The Patient Protection and Affordable Care Act ( ACA ) of 2010, and subsequent related legislation, created a framework for significantly changing the U.S. health insurance marketplace and healthcare delivery. Some provisions of the ACA are focused on changing features of healthcare plans, such as the requirement that health insurance fully cover certain preventive services. Other provisions of the ACA are focused on providing expanded access to healthcare coverage through state or federal exchanges (also called marketplaces ) with income-based federal premium subsidies available to participants. Another important provision of the ACA encourages continued employer-sponsored healthcare coverage. With so many significant changes coming to the U.S. healthcare industry as a result of this act, the cost of healthcare may be significantly affected for years to come. The S&P Healthcare Claims Indices could serve as a key tool to measure and understand these shifts in costs. Key provisions of the ACA include the following: Individual Mandate: Beginning in 2014, all individuals will be required to have health insurance that meets certain minimum requirements. Individuals without coverage will be subject to a tax penalty. Employer Coverage Requirements: Beginning in 2015, employers with 50 or more full-time employee will be subject to a penalty if they do not offer health insurance or an alternative penalty if they offer coverage that does not meet minimum value and affordability requirements. Exchange Development: State exchanges (also called marketplaces ) will need to be created to facilitate the purchase of individual healthcare insurance, with premium subsidies available to individuals/families with income between 100% and 400% of the Federal Poverty Level. These exchanges must be operational by

27 Medicaid Expansion: Federal incentives encourage states to expand Medicaid to all individuals with incomes under 133% of the Federal Poverty Level in Guaranteed Issue Requirements and Insurance Rating Restrictions: The ACA requires guaranteed issue with no rating adjustments for health status and no exclusions of pre-existing conditions. Premium rates can only vary by age (with a maximum ratio of 3:1), smoking status and location. These requirements are generally effective in Other Benefit Requirements: The ACA implements a series of additional benefit requirements, including the following: o Prohibitions on lifetime maximum and annual maximums (lifetime 2011/annual were phased out and fully eliminated in 2014); o Requirements that plans broadly cover essential health benefits (effective 2014); o Requirements that plans provide coverage for dependent children up to the age of 26 (effective 2011); o o o Limits on deductibles and requirement of plans to meet minimum actuarial value requirements (effective 2014) ; Requirements for coverage without a deductible or co-pay for many preventive services (effective 2011); and Wellness Programs: Allow for the expansion of employer-sponsored wellness programs by allowing for credits/penalties of up to 30% of plan premiums (50% for tobacco-related incentives) for employees that do not participate in the wellness program. 5b. Coverage Implications The changes discussed above are expected to result in a large increase in the commercially insured population, with the largest impact on the individual insurance market. The state exchanges, along with federal premium tax subsidies and guaranteed issue requirements, will make the individual market a viable alternative for many participants that are uninsured today. The individual mandate will likely further encourage those that are uninsured to enroll in coverage, either through the exchanges or through an employer-sponsored plan. From the employer perspective, penalties under ACA for not offering coverage may encourage expansion of coverage in certain cases. On the other hand, the existence of a viable individual market may encourage some employers to discontinue their group health plans. Overall, per the Congressional Budget Office (CBO), there is expected to be a net sizeable increase in the insured population beginning in 2014 (see Exhibits 24a-24c). By 2016, the CBO estimates that 25 million U.S. residents who would have had no health insurance coverage prior to the passage of ACA will now have coverage. This represents an approximate 11% increase in the total insured population. 27

28 Estimate of the Effects of the Affordable Care Act on Health Insurance Coverage Exhibit 24a: Number of U.S. Residents Pre-ACA (Millions of residents of the 50 states and the District of Columbia who are younger than 65) Medicaid and CHIP Prior Law Coverage Employment-Based Non-Group and Other Uninsured Total Exhibit 24b: Change in Number of U.S. Residents Post-ACA Medicaid and CHIP Employment-Based 1 * Post-Law Coverage Non-Group and Other Insurance Exchanges * Uninsured Total Exhibit 24c: Number of U.S. Residents Post-ACA Medicaid and CHIP Employment-Based Post-Law Coverage Non-Group and Other Insurance Exchanges Uninsured Total Source: Congressional Budget Office. * Estimate as of May Numbers may not add up to totals because of rounding There are widespread variations in the projected impact of the ACA requirements. For example, a study by the Society of Actuaries ( SOA ) has similarly estimated a significant increase in the insured population due to ACA, with the size of the individual market expected to grow by 115% from 2014 to c. Cost Implications The changes under ACA are likely to have a significant impact on healthcare costs. Broadly, the impact on costs can be thought of along the following lines: Direct Impacts: Expanding insurance coverage to new populations that have generally been uninsured and without any medical underwriting, may drive up costs. This population is often viewed as a group with high risks from a healthcare perspective. For example, it is possible that 4 Cost of the Future Newly Insured under the Affordable Care Act (ACA), Society of Actuaries, March

29 individuals moving from uninsured to insured have foregone preventive services for a period of time and may now be more at risk for higher healthcare claims. Indirect Impacts/Market Forces: As the insured population expands and individuals begin shopping in exchanges for coverage, market forces and competition may push health plans to improve their efficiency and offer alternatives other than the traditional broad-based Preferred Provider Organization ( PPO ) plans that are the predominant plan type today. If these efficiencies and new alternatives occur, the changes could help to push overall costs downward, or at least mitigate cost growth. According to a study by SOA, the direct impact of covering new individuals is expected to be significant, with impact varying by state. Overall, costs in the individual market are expected to increase by about 32% due to the movement of individuals, many of whom are now uninsured, into the individual market. SOA anticipates a high degree of variability on a state-by-state basis, with approximately 43 states experiencing a double-digit increase and/or decrease. Some studies suggest that individual healthcare plans often do not provide the same comprehensive benefits that are required under ACA, and this may account for some of the anticipated increases in costs post-aca. However, having a broader risk pool as a result of ACA expansion of healthcare coverage could potentially have a mitigating impact on the expected increases in healthcare costs. It is important to note that the SOA study is focused on estimating the direct cost of adding new members to the individual group insurance pool. This study does not account for market reactions and plan adjustments to compete in the exchanges. Given that the ACA exchange environment will increase the ability to compare offerings, it is possible that many individuals will be making choices based on price as the primary factor. Exhibit 25 illustrates the results of a study examining a select group of employees who were switched to an exchange and the choices made after comparing healthcare options within that exchange. When employees used the Aon Inc. Corporate Exchange (which differs from, but is setup in a similar format to the public exchanges created under ACA), individuals generally selected plan options based on price, with many employees willing to select a leaner plan if it comes at a favorable price. 5 Exhibit 25: Workers choices changed for 2013 when they used an exchange. Plans with Lower Cost & More Limited Coverage Source: Aon Inc. 5 Source: Aon Inc. Plans with Higher Cost & More Robust Coverage 29

30 As an extension of this, many insurance carriers are looking to institute narrow networks in their exchange offerings. Narrow networks separate physicians and other healthcare providers into tiered networks based on cost of care. These narrow networks are designed to exclude high cost or inefficient healthcare providers, resulting in overall lower healthcare costs. There are several early indications of the development of these networks. Certain hospitals are directly contracted to form narrow networks, and many health plans are also building out these networks 6. The savings that will be generated from these initiatives will impact healthcare costs in the coming years. 5d. Additional Payment Reforms and the Growth of At-Risk Payment Models Throughout the health insurance industry, there is currently a large focus on provider payment reform. Health insurers are moving towards models to reduce their financial risk by shifting towards models where the healthcare providers bear more financial accountability. Health insurers and providers are increasingly moving towards at-risk payment models financing models in which some portion of provider reimbursement is contingent on patient outcomes including financial outcomes. At a basic level, this could include primary care incentives or performance-based contracting. At its most advanced level, value-based contracting traditionally relied on capitation. Because of the limitations of capitation as a payment model, both insurance carriers and healthcare provider organizations have been searching for more effective tools for structuring at-risk payment models. The S&P Healthcare Claims Indices could play a critical role in serving as the underlying measure of healthcare market trend that is used in risk-based payment models. For example, a health carrier and a healthcare provider system could enter into a risk-based contract that shifts the financial risk based on whether the actual cost trend specific to the contract was above or below the market trend (measured by the selected S&P Healthcare Claims Index) for the contract period. For the healthcare provider system, such a contract would provide a financial incentive to hold down healthcare costs, while for the insurance carrier, the contract would limit their financial risk if actual trends exceeded the market trends. Having an independent, current measure of healthcare trend as the basis for settling risk-based contracts could be the critical factor that accelerates the use of risk-based payment models. The spectrum shows the range of at-risk payment options that are being used (see Exhibit 26). Exhibit 26: At-risk payment models. Insurers: Lower Risk Providers: Higher Risk Insurers: Higher Risk Providers: Lower Risk Low Provider Integration and Accountability High Provider Integration and Accountability Source: Aon Inc. 6 Another Big Step in Reshaping Health Care, The Wall Street Journal, March 1,

31 Over time, insurers are expected to move up the spectrum and towards payment mechanisms that are either based on shared risk between the insurer and the provider or some level of capitation payments. As noted above, the S&P Healthcare Claims Indices will be a key component in accelerating this market shift. Past experience with capitation payment models has had mixed results and has shown the limitations of the traditional capitation approach. We believe that the introduction of the S&P Healthcare Claims Indices will result in the opportunity for health carriers and healthcare provider systems to structure more flexible and effective financing models. In addition, as use of the capitation model increases, S&P DJI is reviewing selected capitation data as the basis for a potential set of indices. However, capitation is still an emerging trend and not enough data sets are currently available to create a robust, representative index. 5e. Variability Across Geographic Regions The development of alternative payment models happens at a local level and is a function of provider strength and competition in a given market. As such, value-based contracting/payment arrangements vary significantly by region. The ACA codified the concept of Accountable Care Organizations ( ACO ), which are healthcare provider organizations that provide a full range of health services and will also accept and manage the financial risk of providing services to the covered participants. Exhibit 27 shows how the prevalence of ACOs varies by state. 7 Exhibit 27: Number of ACOs by State Source: Leavitt Partners The varying numbers of ACOs by state illustrate how ACA legislation may have different outcomes for different states. S&P DJI offers indices that measure the changing cost of healthcare at various levels of geographic detail. In addition, custom indices can be created to measure specifically defined geographic areas. 7 Leavitt Partners 31

32 5f. Indices as Tools to Monitor Changes in Healthcare Costs With so many changes underway as a result of recent healthcare legislation and other industry trends, the market increasingly needs a tool to help measure the changes in healthcare, and even more important, the ability to break down and track the origin of those costs. More transparency is needed in the marketplace to understand questions such as: Will the number of insured enrollees grow, and if so, what is the overall impact on healthcare costs? Will there be a shift from employer coverage to individual coverage over time as a result of this legislation? If so, what is the impact on costs for each line of business? Will healthcare trends continue to vary by region? If so, which regions will see the greatest impact as a result of ACA and payment reform? While no single set of data can provide specific answers to each of these complex questions, the S&P Healthcare Claims Indices can be used as a tool to monitor changes in healthcare costs in various market segments. This in turn could provide significant input towards the answers of these and many other questions associated with healthcare reform. 6. Conclusion The rising cost of healthcare services is one of the most serious problems affecting public and private organizations in the U.S. Economists have pointed out that the inflationary pressures inherent in the FFS payment system make it unlikely that overall costs can be contained unless effective tools are available to support a transition to a more efficient system. The S&P Healthcare Claims Indices are an important tool towards improved cost transparency in the healthcare market and could contribute towards more stable reformation of healthcare financing. The indices could serve as a source of valuable information to a wide range of healthcare organizations, public policy makers and other market participants. Since the indices represent a benchmark trend for healthcare costs, they can be used by insurers to analyze past experience trends and thus set future rates. In addition, medical providers can utilize them to help guide forecasts for future revenue growth. Analysts and other industry experts can use them to understand where specific trends in healthcare costs are originating, and how to respond to these trends. Furthermore, they can be used as a basis for providing rate or trend guarantees, or for structuring risk sharing arrangements between health plans, employers and/or providers for annual or longer term periods. The S&P Healthcare Claims Indices will be an essential instrument for any organization that needs to understand the trends in healthcare costs; these groundbreaking indices are the most credible, independent and continually updated source of information on healthcare cost trends available in the market. Historical and current monthly index information, including a factsheet and index methodology, is available at The indices will be published on a monthly basis, with press releases sent to major media outlets quarterly. The indices will be published at: They are also available on Bloomberg. An annual schedule of index release dates will be published on each December for the following year. In order to receive and use the index data, a license from S&P DJI is required. Additional indices and geographical sets are available by subscription only. In order to use the data as part of another product that either enhances an existing product, or generates additional income, a license from S&P DJI is required. Custom indices by geographic area, type of coverage (individual, small group, large group and ASO), and combinations of benefits (for example, physician + pharmaceutical, etc.) can be developed and distributed at an additional cost. For additional information on data licensing, index licensing, customized indices, and associated pricing, please contact: Bo Chung Managing Director

33 APPENDIX A Exhibits A-1 through A-5 illustrate various precision tests utilizing 99% confidence intervals within a 1% margin of error. Exhibit A-1: All Lines of Business - Precision by Location Location Healthcare Medical Drug/Rx Facility Inpatient Outpatient Professional Services US Total 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Census Divisions Northeast 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Midwest 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% South 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% West 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Census Regions New England 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Middle Atlantic 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% East North Central 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% West North Central 1.0% 1.0% 1.0% 1.0% 1.2% 1.0% 1.0% South Atlantic 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% East South Central 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% West South Central 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Mountain 1.0% 1.0% 1.0% 1.0% 1.4% 1.0% 1.0% Pacific 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Selected States New York 1.0% 1.0% 1.0% 1.0% 1.4% 1.0% 1.0% Florida 1.0% 1.0% NA 1.0% 1.1% 1.0% 1.0% Texas 1.0% 1.0% NA 1.0% 1.0% 1.0% 1.0% New Mexico 1.5% 1.6% 2.4% 2.2% 3.9% 2.1% 1.2% Oklahoma 1.0% 1.0% 1.2% 1.3% 2.2% 1.2% 1.0% Washington 1.0% 1.0% 1.1% 1.0% 1.8% 1.0% 1.0% Metropolitan Enrollment as of January 31, 2013 Precision with 99% Confidence Dallas 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Chicago 1.0% 1.0% NA 1.0% 1.0% 1.0% 1.0% Seattle 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Source: S&P Dow Jones Indices and Milliman Inc. Percentages shown signify the precision with 99% confidence in each area with the given enrollment as of 1/31/2013. Areas absent from the list were areas with missing data as of 1/31/2013 (Full list of selected states: NY, FL, TX, NM, OK, WA. Full list of selected healthcare metropolitan regions: Dallas, Chicago, Seattle). Assumes claims are random and approximately normally distributed. Based on Milliman's 2013 Claims Probability Distributions. Distributions assume uniform age, gender, health status, etc. Differences between Milliman s sample sizes and actual results depend on the extent to which actual experience conforms to the assumptions made in this analysis. Actual experience may not conform exactly to the assumptions used in this analysis. 33

34 Exhibit A-2: ASO - Precision by Location Enrollment as of January 31, 2013 Precision with 99% Confidence Location Healthcare Medical Drug/Rx Facility Inpatient Outpatient Professional Services US Total 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Census Divisions Northeast 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Midwest 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% South 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% West 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Census Regions New England 1.0% 1.0% 1.0% 1.0% 1.4% 1.0% 1.0% Middle Atlantic 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% East North Central 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% West North Central 1.0% 1.0% 1.1% 1.0% 1.6% 1.0% 1.0% South Atlantic 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% East South Central 1.0% 1.0% 1.0% 1.0% 1.2% 1.0% 1.0% West South Central 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Mountain 1.0% 1.0% 1.2% 1.0% 1.8% 1.0% 1.0% Pacific 1.0% 1.0% 1.0% 1.0% 1.2% 1.0% 1.0% Selected States New York 1.0% 1.0% 1.4% 1.1% 1.8% 1.0% 1.0% Florida 1.0% 1.0% 1.3% 1.0% 1.8% 1.0% 1.0% Texas 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% New Mexico 1.9% 2.0% 3.6% 2.8% 4.9% 2.6% 1.5% Oklahoma 1.3% 1.5% 2.3% 2.0% 3.5% 1.9% 1.1% Washington 1.0% 1.0% 1.7% 1.3% 2.3% 1.2% 1.0% Metropolitan Dallas 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Chicago 1.0% 1.0% 1.5% 1.0% 1.0% 1.0% 1.0% Seattle 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Source: S&P Dow Jones Indices and Milliman Inc. Percentages shown signify the precision with 99% confidence in each area with the given enrollment as of 1/31/2013. Areas absent from the list were areas with missing data as of 1/31/2013 (Full list of selected states: NY, FL, TX, NM, OK, WA. Full list of selected healthcare metropolitan regions: Dallas, Chicago, Seattle). Assumes claims are random and approximately normally distributed. Based on Milliman's 2013 Claims Probability Distributions. Distributions assume uniform age, gender, health status, etc. Differences between Milliman s sample sizes and actual results depend on the extent to which actual experience conforms to the assumptions made in this analysis. Actual experience may not conform exactly to the assumptions used in this analysis. 34

35 Exhibit A-3: Large Group - Precision by Location Enrollment as of January 31, 2013 Precision with 99% Confidence Location Healthcare Medical Drug/Rx Facility Inpatient Outpatient Professional Services US Total 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Census Divisions Northeast 1.0% 1.0% 1.0% 1.0% 1.6% 1.0% 1.0% Midwest 1.0% 1.0% 1.0% 1.0% 1.6% 1.0% 1.0% South 1.0% 1.0% 1.0% 1.0% 1.4% 1.0% 1.0% West 1.0% 1.0% 1.0% 1.1% 1.9% 1.0% 1.0% Census Regions New England 1.0% 1.2% 1.1% 1.6% 2.8% 1.5% 1.0% Middle Atlantic 1.0% 1.0% 1.0% 1.1% 2.0% 1.1% 1.0% East North Central 1.0% 1.0% 1.0% 1.0% 1.8% 1.0% 1.0% West North Central 1.2% 1.4% 1.4% 1.9% 3.3% 1.8% 1.0% South Atlantic 1.0% 1.0% 1.0% 1.1% 1.8% 1.0% 1.0% East South Central 1.3% 1.5% 1.5% 2.0% 3.5% 1.9% 1.1% West South Central 1.0% 1.0% 1.0% 1.4% 2.5% 1.3% 1.0% Mountain 1.5% 1.6% 1.5% 2.2% 3.8% 2.1% 1.1% Pacific 1.0% 1.0% 1.0% 1.3% 2.3% 1.2% 1.0% Selected States New York 1.2% 1.4% 1.5% 1.9% 3.3% 1.8% 1.0% Florida 1.1% 1.2% 1.1% 1.6% 2.9% 1.5% NA Texas 1.0% 1.2% 1.1% 1.6% 2.8% 1.5% NA New Mexico 5.8% 6.4% 6.3% 8.7% 15.3% 8.2% 4.6% Oklahoma 2.5% 2.8% 2.8% 3.8% 6.7% 3.6% 2.0% Washington 1.8% 1.9% 1.9% 2.6% 4.6% 2.5% 1.4% Metropolitan Dallas 1.6% 1.8% 1.8% 2.5% 4.3% 2.3% 1.3% Chicago 1.6% 1.7% 1.1% 2.4% 4.1% 2.2% 1.2% Seattle 1.7% 1.9% 2.0% 2.6% 4.6% 2.5% 1.4% Source: S&P Dow Jones Indices and Milliman Inc. Percentages shown signify the precision with 99% confidence in each area with the given enrollment as of 1/31/2013. Areas absent from the list were areas with missing data as of 1/31/2013 (Full list of selected states: NY, FL, TX, NM, OK, WA. Full list of selected healthcare metropolitan regions: Dallas, Chicago, Seattle). Assumes claims are random and approximately normally distributed. Based on Milliman's 2013 Claims Probability Distributions. Distributions assume uniform age, gender, health status, etc. Differences between Milliman s sample sizes and actual results depend on the extent to which actual experience conforms to the assumptions made in this analysis. Actual experience may not conform exactly to the assumptions used in this analysis. 35

36 Exhibit A-4: Small Group - Precision by Location Enrollment as of January 31, 2013 Precision with 99% Confidence Location Healthcare Medical Drug/Rx Facility Inpatient Outpatient Professional Services US Total 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Census Divisions Northeast 1.0% 1.0% 1.0% 1.1% 1.9% 1.0% 1.0% Midwest 1.0% 1.0% 1.0% 1.2% 2.1% 1.1% 1.0% South 1.0% 1.0% 1.0% 1.0% 1.4% 1.0% 1.0% West 1.0% 1.0% 1.0% 1.3% 2.3% 1.2% 1.0% Census Regions New England 1.2% 1.3% 1.2% 1.8% 3.1% 1.7% 1.0% Middle Atlantic 1.0% 1.0% 1.0% 1.4% 2.5% 1.3% 1.0% East North Central 1.0% 1.0% 1.0% 1.4% 2.4% 1.3% 1.0% West North Central 1.5% 1.6% 1.6% 2.2% 3.9% 2.1% 1.2% South Atlantic NA NA 1.0% 1.2% 2.2% 1.2% NA East South Central 1.5% 1.7% 1.6% 2.3% 4.0% 2.1% 1.2% West South Central NA 1.0% NA 1.4% 2.4% 1.3% NA Mountain 1.9% 2.1% 2.1% 2.9% 5.0% 2.7% 1.5% Pacific 1.0% 1.1% 1.0% 1.5% 2.6% 1.4% 1.0% Selected States New York 2.5% 2.7% 4.2% 3.7% 6.4% 3.5% 1.9% Florida 1.3% 1.5% 1.4% 2.0% 3.5% 1.9% 1.1% Texas 1.0% 1.2% 1.0% 1.6% 2.7% 1.5% NA New Mexico 6.2% 6.9% 6.6% 9.3% 16.3% 8.8% 4.9% Oklahoma 2.4% 2.7% 2.6% 3.6% 6.3% 3.4% 1.9% Washington 2.8% 3.1% 3.1% 4.2% 7.4% 4.0% 2.2% Metropolitan Dallas 1.4% 1.5% 1.5% 2.1% 3.7% 2.0% 1.1% Chicago 1.2% 1.3% NA 1.8% 3.1% 1.7% NA Seattle 3.2% 3.6% 3.6% 4.9% 8.5% 4.6% 2.6% Source: S&P Dow Jones Indices and Milliman Inc. Percentages shown signify the precision with 99% confidence in each area with the given enrollment as of 1/31/2013. Areas absent from the list were areas with missing data as of 1/31/2013 (Full list of selected states: NY, FL, TX, NM, OK, WA. Full list of selected healthcare metropolitan regions: Dallas, Chicago, Seattle). Assumes claims are random and approximately normally distributed. Based on Milliman's 2013 Claims Probability Distributions. Distributions assume uniform age, gender, health status, etc. Differences between Milliman s sample sizes and actual results depend on the extent to which actual experience conforms to the assumptions made in this analysis. Actual experience may not conform exactly to the assumptions used in this analysis. 36

37 Exhibit A-5: Individual - Precision by Location Enrollment as of January 31, 2013 Precision with 99% Confidence Location Healthcare Medical Drug/Rx Facility Inpatient Outpatient Professional Services US Total 1.0% 1.0% 1.0% 1.0% 1.2% 1.0% 1.0% Census Divisions Northeast 1.5% 1.6% 1.6% 2.2% 3.9% 2.1% 1.2% Midwest 1.0% 1.0% 1.2% 1.4% 2.5% 1.3% 1.0% South 1.0% 1.0% 1.0% 1.0% 1.8% 1.0% 1.0% West 1.0% 1.0% 1.0% 1.3% 2.3% 1.3% 1.0% Census Regions New England 2.4% 2.6% 2.7% 3.6% 6.3% 3.4% 1.9% Middle Atlantic 1.9% 2.1% 1.9% 2.8% 4.9% 2.7% 1.5% East North Central 1.1% 1.2% 1.6% 1.7% 3.0% 1.6% 1.0% West North Central 1.8% 2.0% 1.9% 2.7% 4.7% 2.5% 1.4% South Atlantic 1.0% 1.0% 1.1% 1.4% 2.4% 1.3% 1.0% East South Central 1.8% 2.0% 1.9% 2.7% 4.7% 2.5% 1.4% West South Central 1.2% 1.3% 1.2% 1.7% 3.1% 1.6% 1.0% Mountain 2.1% 2.3% 2.3% 3.2% 5.6% 3.0% 1.7% Pacific 1.0% 1.1% 1.7% 1.5% 2.6% 1.4% 1.0% Selected States New York 12.5% 13.8% 15.5% 18.8% 32.9% 17.7% 9.9% Florida 1.2% 1.3% 1.3% 1.8% 3.2% 1.7% 1.0% Texas 1.2% 1.3% 1.3% 1.8% 3.2% 1.7% 1.0% New Mexico 5.8% 6.4% 6.2% 8.7% 15.3% 8.2% 4.6% Oklahoma 3.4% 3.7% 4.5% 5.1% 8.9% 4.8% 2.7% Washington 17.0% 18.7% 23.7% 25.5% 44.6% 24.0% 13.5% Metropolitan Dallas 1.8% 2.0% 2.1% 2.7% 4.8% 2.6% 1.4% Chicago 1.7% 1.9% 2.3% 2.6% 4.6% 2.5% 1.4% Seattle 20.9% 23.0% 28.7% 31.3% 54.8% 29.5% 16.5% Source: S&P Dow Jones Indices and Milliman Inc. Percentages shown signify the precision with 99% confidence in each area with the given enrollment as of 1/31/2013. Areas absent from the list were areas with missing data as of 1/31/2013 (Full list of selected states: NY, FL, TX, NM, OK, WA. Full list of selected healthcare metropolitan regions: Dallas, Chicago, Seattle). Assumes claims are random and approximately normally distributed. Based on Milliman's 2013 Claims Probability Distributions. Distributions assume uniform age, gender, health status, etc. Differences between Milliman s sample sizes and actual results depend on the extent to which actual experience conforms to the assumptions made in this analysis. Actual experience may not conform exactly to the assumptions used in this analysis. 37

38 Glossary Accountable Care Organization - A group of health care providers who give coordinated care, chronic disease management, and thereby improve the quality of care patients get. The organization's payment is tied to achieving health care quality goals and outcomes that result in cost savings. Affordable Care Act - The comprehensive health care reform law enacted in March The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 and was amended by the Health Care and Education Reconciliation Act on March 30, The name Affordable Care Act is used to refer to the final, amended version of the law. Allowed Charges - Maximum amount on which payment is based for covered health care services. This may be called eligible expense, payment allowance" or "negotiated rate." If your provider charges more than the allowed amount, you may have to pay the difference. Brand Name Drugs - A drug sold by a drug company under a specific name or trademark and is protected by a patent. Brand name drugs may be available by prescription or over the counter. Claim - A request for payment that you or your health care provider submits to your health insurer when you get items or services you think are covered. Co-Payment - A fixed amount (for example, $15) you pay for a covered health care service, usually when you get the service. The amount can vary by the type of covered health care service. Cost-Sharing - The share of costs covered by your insurance that you pay out of your own pocket. This term generally includes deductibles, coinsurance, and co-payments, or similar charges, but it doesn't include premiums, balance billing amounts for non-network providers, or the cost of non-covered services. Cost sharing in Medicaid and CHIP also includes premiums. Current Procedural Terminology Manual (CPT) This is a medical code set maintained by the American Medical Association through the CPT Editorial Panel. The CPT code set (copyright protected by the AMA) describes medical, surgical, and diagnostic services and is designed to communicate uniform information about medical services and procedures among physicians, coders, patients, accreditation organizations, and payers for administrative, financial, and analytical purposes. Deductible - The amount you owe for health care services your health insurance or plan covers before your health insurance or plan begins to pay. For example, if your deductible is $1,000, your plan won t pay anything until you ve met your $1,000 deductible for covered health care services subject to the deductible. The deductible may not apply to all services. Exchange (Health Insurance Marketplace) - A resource where individuals, families, and small businesses can learn about their health coverage options; compare health insurance plans based on costs, benefits, and other important features; choose a plan; and enroll in coverage. The Marketplace also provides information on programs that help people with low to moderate income and resources pay for coverage. This includes ways to save on the monthly premiums and out-of-pocket costs of coverage available through the Marketplace, and information about other programs, including Medicaid and the Children s Health Insurance Program (CHIP). The Marketplace encourages competition among private health plans, and is accessible through websites, call centers, and in-person assistance. In some states, the Marketplace is run by the state. In others it is run by the federal government. Fee For Service - A method in which doctors and other health care providers are paid for each service performed. Examples of services include tests and office visits. Facility or Hospital. An institution for the treatment, care, and cure of the sick and wounded, for the study of disease, and for the training of physicians, nurses, and allied health care personnel. Generic Drug - A prescription drug that has the same active-ingredient formula as a brand-name drug. Generic drugs usually cost less than brand-name drugs. The Food and Drug Administration (FDA) rates these drugs to be as safe and effective as brand-name drugs. 38

39 Group Health Plan - In general, a health plan offered by an employer or employee organization that provides health coverage to employees and their families. Health Insurance - A contract that requires your health insurer to pay some or all of your health care costs in exchange for a premium. Health Maintenance Organization (HMO) - a comprehensive prepaid system of health care intended to have emphasis on the prevention and early detection of disease, and continuity of care; often used synonymously with managed care plan. Hospital Inpatient Care - Health care that you get when you're admitted as a patient to a health care facility, like a hospital or skilled nursing facility. Hospital Outpatient Care - Care in a hospital that usually doesn t require an overnight stay. Individual Health Insurance Policy - Policies for people that aren't connected to job-based coverage. Individual health insurance policies are regulated under state law. Large Group Health Plan - In general, a group health plan that covers employees of an employer that has 101 or more employees. Until 2016, in some states large groups are defined as 51 or more. Margin of error - In statistics, margin of error is a measurement of the accuracy of the results of a survey. For example, the larger the margin of error around an estimated value, the less accurate is the estimated value. Physician Services (Professional Services) - Health care services a licensed medical physician (M.D. Medical Doctor or D.O. Doctor of Osteopathic Medicine) provides or coordinates. Preferred Provider Organization (PPO)- A preferred provider organization is a subscription-based medical care arrangement. A membership allows a substantial discount below the regularly charged rates of the designated professionals partnered with the organization. Prescription Drugs - Drugs and medications that by law are required to be prescribed by a doctor. Preventive Services - Routine health care that includes screenings, check-ups, and patient counseling to prevent illnesses, disease, or other health problems. Self Insured Plan (ASO) - Type of plan usually present in larger companies where the employer itself collects premiums from enrollees and takes on the responsibility of paying employees and dependents medical claims. These employers can contract for insurance services such as enrollment, claims processing, and provider networks with a third party administrator, or they can be self-administered. Small Group Health Coverage - In general, a group health plan that covers employees of an employer that has 100 or less employees. Until 2016, in some states small groups are defined as 50 or less. Sources: and 39

40 About S&P Dow Jones Indices S&P Dow Jones Indices LLC, a part of McGraw Hill Financial, is the world s largest, global resource for indexbased concepts, data, and research. Home to iconic financial market indicators, such as the S&P 500 and the Dow Jones Industrial Average, S&P Dow Jones Indices LLC has over 115 years of experience constructing innovative and transparent solutions that fulfill the needs of investors. More assets are invested in products based upon our indices than any other provider in the world. With over 830,000 indices covering a wide range of asset classes across the globe, S&P Dow Jones Indices LLC defines the way investors measure and trade the markets. For more articles on a broad range of index-related topics or to sign up to receive periodic updates, visit us at Like What You Read? Sign up to receive complimentary updates on a broad range of index-related topics and events brought to you by S&P Dow Jones Indices. 40

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