Using the Medicare Current Beneficiary Survey to conduct research on Medicare-eligible veterans.
Health insurance industry
Ripley, Diane Cowper
|Publication:||Name: Journal of Rehabilitation Research & Development Publisher: Department of Veterans Affairs Audience: Academic Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2010 Department of Veterans Affairs ISSN: 0748-7711|
|Issue:||Date: Dec 30, 2010 Source Volume: 47 Source Issue: 8|
|Product:||Product Code: E198380 Veterans SIC Code: 6321 Accident and health insurance|
|Geographic:||Geographic Scope: Minnesota Geographic Code: 1U4MN Minnesota|
Subject to eligibility guidelines, veterans who have served on Active Duty in the U.S. Armed Forces, in the military reserves, or in the National Guard are entitled to receive medical care through a nationwide network of Veterans Health Administration (VHA) facilities in the Department of Veterans Affairs (VA). The VHA is one of the world's largest healthcare systems, spending approximately $40 billion in fiscal year (FY) 2008 (i.e., October 1, 2007-September 30, 2008) to provide healthcare to over 5 million veterans, or about 22 percent of the nation's veterans . While the VHA was initially focused on providing inpatient care, this focus was broadened to include the full spectrum of care, including outpatient and pharmaceutical services, in the 1990s . Medicare-eligible veterans are an exclusive group in that they are dually eligible to receive comparable sets of services through two Federally funded programs, Medicare and the VHA [7-15]. Medicare covers health services provided by the private sector, whereas the VHA serves as an important safety net [16-23], especially for veterans who are disabled and service connected (SC) for injuries or disease incurred or aggravated in military service, in lower socioeconomic status, in poorer health, and/or suffering from chronic conditions [24-27].
Datasets facilitating the conduct of research addressing the utilization and cost of services provided to veterans in multiple sectors of the healthcare market are limited. Sponsored by the Centers for Medicare and Medicaid Services (CMS), the Medicare Current Beneficiary Survey (MCBS) dataset is unique in that it offers researchers access to the full spectrum of public and private sector healthcare utilization and costs, along with measures of health and functional status, access to care, and patient satisfaction for a nationally representative sample of Medicare beneficiaries [28-29]. The MCBS dataset contains a set of questions identifying veterans who have served in the U.S. Armed Forces, including their period of service, SC status, and VHA medical care service use. Because survey respondents are followed for a minimum of 4 years and the survey has been conducted since 1992, the MCBS dataset serves as a multipurpose panel survey that can support both cross-sectional and longitudinal analyses of the size of the safety net provided by the VHA to Medicare-eligible veterans. Thus, the MCBS is a rich, comprehensive data source uniquely suited to addressing VHA and Medicare policy-related questions, such as the effect of changes in program eligibility on utilization and costs, the effect of opening new facilities on access to care, and the potential benefits of comanaging care.
Adler  and Eppig and Chulis  describe the utility of the MCBS dataset for general research purposes. Studies using the MCBS dataset for research involving veteran subjects have addressed the effect of VHA eligibility reform on veteran Medicare beneficiaries' healthcare utilization and cost, the use of prescription drugs by veterans with diabetes, and the use of preventive services by elderly male veterans [31-33].
The primary objective of this study is to address the utility of using the MCBS dataset for research focused on Medicare-eligible veterans' utilization and cost of VHA and Medicare health services. Our specific aims are to--
1. Assess the completeness of the MCBS dataset by describing the degree of missing MCBS data for variables typically used in economic analyses predicting utilization and cost.
2. Compare the socioeconomic characteristics of the MCBS sample of veterans to the National Survey of Veterans (NSV) and address the MCBS sample's representativeness.
3. Validate the self-reported utilization and imputed cost estimates by addressing the degree of concordance between MCBS, VHA, and Medicare Fee-For-Service (FFS) administrative datasets regarding:
a. Veterans' use of VHA services (person-level estimates).
b. Veterans' levels of VHA and Medicare FFS services used (event-level estimates).
c. CMS's imputed VHA cost estimates in the MCBS and VHA Health Economics Resource Center (HERC) average costs (considering per capita and event-level costs).
In the "Methods" section, we describe the sample population, the exclusion criteria, and the datasets used to address each of the study aims (i.e., the MCBS, NSV, and VHA administrative datasets). We then present the results of the analyses addressing each of the study aims, followed by a discussion of potential solutions to the problems encountered for research purposes.
Sample Population and Exclusion Criteria
CMS selected the MCBS sample from Medicare enrollment files to be representative of the Medicare population. The sample was selected using a stratified, unequal probability, multistage probability design that consists of aged and disabled beneficiaries enrolled in Medicare Part A (hospital insurance), Part B (medical insurance), or both and residing in households or long-term care facilities in the United States and Puerto Rico. Disabled persons aged <65 and very old persons aged >85 are oversampled. ** MCBS respondents are typically followed for 4 years. Our study sample contains approximately 29,756 person-years of data on veterans identified over an 11-year time span (1992-2002). Assuming that researchers using the MCBS data are interested primarily in analyzing veterans' utilization of services and their associated costs and/or health outcomes, determining a veteran's level of eligibility for accessing VHA services is critical. To illustrate this point, within the time frame of this study, the eligibility of non-SC means-tested veterans (i.e., VHA Priority Groups 7 and 8) has changed over the years, with eligibility granted in 1995, rescinded in 2003, and (partially) restored in 2009. SC veterans, on the other hand, historically have received priority status for VHA services. Thus, SC is the primary determinant (besides household income and assets required for VHA means testing) for establishing VHA eligibility and enabling access to VHA services. If veteran respondents did not answer the question regarding their SC status, we excluded them from the sample (n = 170). Since they represent <1 percent of the sample (170/29,756 = 0.6%), this exclusion criteria did not significantly affect the representativeness or usefulness of the sample for analyzing veterans' utilization and costs.
Because the underlying health conditions of patients living in long-term care facilities lead to patterns of healthcare utilization and cost that differ significantly from community-dwelling veterans, we also excluded the institutionalized veterans (n = 1,080). The remaining sample of 28,506 person-years of data translates into data on 11,121 (unique) community-dwelling veterans, with 11 to 24 percent using VHA services between 1992 and 2002 (Table 1).
Addressing Study Aim 1: Assessing Completeness of Medicare Current Beneficiary Survey Dataset
The MCBS dataset consists of two files: the Access to Care files and the Cost and Use files. Data from the Access to Care files provide detailed information on the health and socioeconomic characteristics of beneficiaries, access to care measures, and satisfaction with medical care services received. The Cost and Use files contain a combination of survey-reported data, Medicare FFS claims data, and other data from CMS's administrative files. Data from the Cost and Use files have undergone a careful reconciliation process to identify healthcare services reported from bills and self-reported survey data. The files provide a complete account of all medical and pharmaceutical care services received, out-of-pocket expenses, amounts paid and/or covered by all third-party payers, and sources of coverage or payment, including the VHA. The files also contain information on long-term care services, supplementary health insurance, living arrangements, income, health status, and physical functioning. Thus, the Cost and Use files can support a much broader range of research and policy analyses on the veteran Medicare population than would be possible using either self-reported survey data or administrative billing data alone.
Since MCBS respondents are interviewed three times a year, the average interview recall period is about 4 months. Given normal rates of memory decay and the frequency with which older people and individuals with disability use medical care, underreporting of medical services is a problem. For Medicare FFS beneficiaries, CMS used Medicare FFS administrative claims data to flag self-reported and FFS comparable events, adjust for underreporting, and validate MCBS self-reported payment amounts. This event-level validation procedure employed "strength of evidence" criteria and hierarchical algorithms described in the MCBS technical documentation .
For health services not covered by Medicare FFS plans, including services provided to individuals enrolled in Medicare Advantage plans and/or Medicaid, no independent source of data could be used to match and verify use and payment information. When payment amounts were not reported by survey respondents within the MCBS sample population, CMS used a computer-intensive iterative imputation technique to fill in missing payment data for likely payers and sources of coverage . The MCBS utilization and cost data are organized to reflect the unit of observation as a person (calendar) year. For researchers interested in predicting utilization and cost, the MCBS dataset contains a set of socioeconomic variables, insurance coverage, indications of health and functional status, and chronic conditions typically used for risk adjustment purposes.
Socioeconomic variables include sex, age, race, marital status, education, income, and family size. Since the MCBS dataset has the patients' addresses and zip codes, researchers can calculate the distance traveled to the nearest VHA medical facility as a measure of veterans' time and travel costs associated with seeing a VHA healthcare provider. *** Veterans' sources of insurance coverage other than Medicare FFS may include supplemental(Medigap) insurance coverage, enrollment in Medicare Advantage Plans, Medicaid, and/or the VHA at any time during the past calendar year. Researchers can create variables indicating the numbers of months per year with Medicare Part A only (not enrolled in Part B), Medigap coverage, and prescription drug coverage.
Measures of health status in any given year include VHA SC disability (Yes/No) and rating (scale of 0-100), general health status (excellent, very good, good, fair, or poor), functional status, chronic conditions, current and former smoking status, and death. Measures of functional status include activities of daily living (scale of 0-5) and independent activities of daily living (scale of 0-6), where higher scores indicate lower health status. Chronic conditions include heart condition, hypertension, stroke, cancer (including skin), diabetes, arthritis, lung disease, Alzheimer disease, and mental illness.
In terms of using these variables in economic analyses of utilization and costs, we address the completeness of the dataset by describing the percentage of the data that is missing.
Addressing Study Aim 2: Comparing Socioeconomic Characteristics of National Survey of Veterans and Medicare Current Beneficiary Survey Samples
The 2001 NSV is the fifth in a series of periodic comprehensive surveys conducted by the VA targeting noninstitutionalized veterans of the U.S. uniformed services living in private households in the United States, including Puerto Rico. The NSV serves as the VA's primary data source for describing veterans' military background, education and training, healthcare usage, and use of a broad array of VA benefits .
The NSV utilizes a dual-frame sample design consisting of a random-digit dialing sample of noninstitutionalized veterans living in the United States and Puerto Rico with (landline) telephone numbers and a list sample of veterans in the VHA Healthcare enrollment file and the Veterans Benefits Administration Compensation and Pension file. One of the questions in the NSV addresses health insurance and asks the respondent, "Are you currently covered by Medicare?" This question was revised in 2001 to distinguish between Medicare Parts A and B coverage as follows: "Medicare Part A pays for hospital care. Are you currently covered by Medicare Part A?" and "Medicare Part B pays for visits to doctor offices. Are you currently covered by Medicare Part B?" If the respondent is aged >65 and/or confirms coverage through Medicare, we included them in our analysis of Medicare-eligible veterans.
While approximately 35 percent of veterans were classified as eligible for Medicare in 1992, the Medicare-eligible population grew to 41 percent of veterans in 2001. In this study, we compared the demographic characteristics of the sample of veteran respondents in the 1992 and 2001 NSV to the Medicare-eligible veterans within the MCBS Cost and Use Files for 1992 and 2001, respectively, by using tests for differences in means and proportions.
Addressing Study Aim 3(a-b): Validating Self-Reported Use of Veterans Health Administration and Medicare Fee-For-Service Services
Since validation studies generally find that self-reported healthcare utilization measures are consistently underreported [35-40], we compared self-reported use of VHA services in the MCBS dataset with events recorded in VHA administrative datasets by linking these data with use of a crosswalk file provided by the VA Information Resource Center . We then identified veterans in the MCBS dataset as VHA users and nonusers by querying their use of VHA medical care facilities and verifying their self-reported use with that found in VHA administrative databases. As mentioned, CMS used Medicare FFS administrative claims data to flag self-reported and FFS comparable events. We used these flags to determine how often veteran Medicare FFS beneficiaries were not reporting (i.e., underreporting) FFS events and compared them with VHA underreporting rates.
Although we could have analyzed inpatient hospitalizations and outpatient count data for the entire time period that the MCBS dataset had been collected, we limited our analyses to the years the VHA cost datasets were available. These limitations are described next.
Reflecting the VHA's evolution from a primarily hospital-based system to one providing the full spectrum of healthcare services, the VHA went through a series of administrative changes that included a commitment to improving the quality of their administrative datasets and reconfiguring the files in the mid to late 1990s. For services prior to FY1997 (i.e., before October 1, 1996), the inpatient Patient Treatment File (PTF) and outpatient care (OPC) SAS files can be used to verify self-reported use of VHA inpatient and outpatient services, respectively; Medical SAS datasets generated from the PTF and National Patient Care Database (NPCD) can be used to verify post-FY1997 inpatient and outpatient utilization, respectively (Table 2) . The Medical SAS datasets are national files containing information on all outpatient healthcare visits, including date of visit, patient characteristics (such as age and sex), type of clinic visited, and diagnostic (International Classification of Diseases-9th revision, Clinical Modification) and Current Procedure and Terminology (CPT) codes. Prior to FY1997, the OPC SAS file contains limited information on the type of facility where care was received (station codes), where care was received within that facility (stop codes), and the number of visits (and stops) by facility. While the VHA began to consistently record diagnostic and procedure codes in FY1997, the quality of this diagnostic information before FY1997 is poor. However, since studies analyzing changes in the number of overall outpatient visits, as well as primary care, specialty, surgical, and mental health care visits pre and post VHA reform efforts rely on clinic stop code information and not diagnostic and procedure codes, they are still enabled.
Since the VHA system is not comparable to the private system in the way clinic visits are scheduled (multiple clinical stops often are scheduled during a patient's 1-day visit to a VHA medical facility) and the way copayments are assessed (one copayment is assessed per any 1-day visit to a VHA facility), some ambiguity exists in what VHA patients perceive and self-report as an outpatient event. While some patients may report having seen three different VHA providers (i.e., three clinic stops and therefore three outpatient events) on any given day, for example, others may report a 1-day visit as one outpatient event regardless of the number of providers (clinic stops) they have seen that day. Thus, in addition to survey respondents' recall error, some measurement error undoubtedly exists. When analyzing the MCBS self reported data, analysts have no way of knowing a priori whether the veteran respondent had been reporting clinic stops or day visits. Additionally, patients reporting when a specific outpatient event occurred may be off by a few days. The combination of measurement and recall errors has no discernable pattern, i.e., the errors do not consistently bias the utilization counts in any one direction. Therefore, establishing a set of rules that data analysts can use to sort out measurement error from recall error and matching a self-reported outpatient event to that found in the VHA administrative databases are very difficult, if not impossible. Thus, in this study, we compared the underlying distributional attributes of 1-day outpatient visits to a VHA facility as well as clinical events defined by clinic stop codes in the VHA's NPCD to the self-reported outpatient events found in the MCBS datasets.
The VHA began collecting prescription drug utilization data across all VHA sites in FY1999 (i.e., October 1, 1998), creating what is known as the VHA's Pharmacy Benefits Management (PBM) database . Since prescription drug use was not consistently recorded in the Veterans' Health Information Systems Architecture before FY1999, nor have the data been consistently retained online for many years, no reliable source of VHA prescription drug utilization data at the national level is available before FY1999. Consequently, our analysis of the discrepancies between self-reported prescription drug use and prescription drug use found in the PBM are limited to FY1999 onward.
While very little ambiguity exists in how patients define and report hospitalizations, a variety of standardization issues come up when prescriptions are enumerated. For example, how does one account for 90- versus 30-day prescriptions, differences in dosages, and refills versus new prescriptions? Since patients are highly unlikely to consider these differences when asked to report their prescriptions, we took a simplistic approach and did not differentiate PBM prescriptions by pill quantity or dose.
Addressing Study Aim 3(c): Validating Centers for Medicare and Medicaid Services' Imputed Veterans Health Administration Costs
Since the VHA does not bill veterans for the services they receive, national VHA claims databases containing estimates of the cost of specific VHA events do not exist. The development of the VHA's HERC inpatient and outpatient cost estimates in FY1998 (i.e., October 1, 1997) onward facilitated the validation of CMS imputed costs in this study .
The HERC inpatient estimates represent the national average cost of a hospital stay for a given diagnosis related group, length of stay (LOS), and days in intensive care. The inpatient estimates are based on analyses of Medicare FFS cost-adjusted charges for veteran stays in non-VHA hospitals. The HERC outpatient file estimates represent the hypothetical average Medicare reimbursement for the CPT codes associated with the VHA visit. Hence, the resources used to provide VHA OPC are assumed to be proportionate to the relative values assigned in the Medicare reimbursement. Both inpatient and outpatient estimates are adjusted so that they tally up to annual national VHA expenditures by type of care [43-44].
The costs associated with VHA OPC before FY1998 are difficult to estimate because of the lack of relevant, consistent information on outpatient utilization in the VHA administrative files noted earlier. Without the added information associated with the diagnostic and procedural information, the cost estimates would have reflected the cost of an average stop code rather than average event-level costs and the variance associated with pre-FY1998 VHA outpatient cost estimates would be biased downwards. Thus, our validation of CMS's imputed VHA outpatient cost estimates was limited to FY1998 onward.
Study Aim 1: Assessing Completeness of Medicare Current Beneficiary Survey Dataset
In terms of the set of potential predictors of healthcare utilization and cost, issues with item nonresponse rates (i.e., missing data) were negligible. Out of the MCBS sample of 28,506 person-years, the data on age, sex, race, family size, household income, ever smoked, and insurance status (Medicare Advantage plans, Medicaid, supplemental policies) were 100 percent complete. Of the remaining set of potential predictors, <1 percent were missing: service era (n = 35 out of 28,506, or 0.12%); marital status (n = 11, 0.04%); educational status (n = 113, 0.40%); general health status (n = 87, 0.31%); current smoker (n = 33, 0.12%); and chronic conditions including heart condition, hypertension, stroke, cancer (including skin), diabetes, arthritis, lung disease, Alzheimer disease, and mental illness (range n = 1-11, maximum 0.04%). Overall, this set of potential predictors resulted in a total of 776 person-years (2.7%) of missing data.
Study Aim 2: Comparing Socioeconomic Characteristics of National Survey of Veterans and Medicare Current Beneficiary Survey Samples
For sex, age, and race, the demographic characteristics of the sample of Medicare-eligible veterans in the MCBS dataset were comparable to those in the 1992 and 2001 NSVs (Table 3). Of veterans in both samples and years, >96 percent were male, 92 to 93 percent of the sample were aged >65 years, and 6 to 7 percent were between 45 and 64 years. Racially, the vast majority of individuals were white (91%); 6 to 7 percent were African American. However, in both 1992 and 2001, the MCBS samples of veterans were less likely to be married, more likely to be widowed, more likely to report being in very good to excellent health, and less likely to report being in fair or poor health. Although the NSV and MCBS samples also differed in terms of their level of education, SC ratings, and use of VHA services in each time period, these differences were not consistent across the 1992 and 2001 time periods. We discuss possible reasons for differences between the survey populations in the "Discussion" section.
Study Aim 3(a): Validation of Self-Reported Use of Veterans Health Administration Services (Person Level)
At the group (sample population) level, the percentage of MCBS veterans who reported using VHA inpatient, outpatient, and prescription services was consistently underidentified in the MCBS dataset compared with the VHA administrative records. The largest discrepancies were for outpatient events. While 14 to 25 percent of MCBS veterans had VHA outpatient administrative claims in calendar years 1998 and 2002, respectively, only 11 to 17 percent self-reported using VHA outpatient services (Table 4).
Study Aim 3(b): Validation of Self-Reported Levels of Veterans Health Administration and Medicare Fee-For-Service Use (Event Level)
For veterans who had used Medicare and/or VHA services during the year, the number and percentage of events that were not reported (i.e., underreported) varied by type of care and by sector (Table 5). While underreporting was less of a problem for VHA than Medicare inpatient stays, veterans tended to underreport more VHA than Medicare outpatient events, regardless of whether a VHA outpatient event was defined as a day visit or a clinic stop. Underreporting was further amplified when VHA outpatient events were defined by clinic stops. The average number of self-reported VHA inpatient stays, outpatient clinic stops, outpatient day visits, and prescriptions were consistently underreported compared with VHA administrative counts (Figures 1-4, respectively).
Study Aim 3(c): Validation of Centers for Medicare and Medicaid Services' Imputed Veterans Health Administration Costs
At the person level, we found large discrepancies between VHA and MCBS dataset estimates of average annual inpatient costs per capita (Table 6). The number of inpatient days per year was also underreported in the MCBS relative to VHA administrative datasets. The lower costs associated with VHA inpatient stays were further magnified by the underreporting issues realized in the utilization of VHA services by MCBS respondents (Figures 1 and 5).
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At the event level, CMS's imputed VHA cost estimates were approximately $11,000 to $16,500 below the HERC's cost estimates for matching inpatient stays (Table 7). Even when the costs covered by all third-party payers involved in reimbursing the VHA inpatient stays was considered, CMS's imputed total cost estimates were still $3,000 to $15,000 lower than the HERC's cost estimates. Since inpatient costs are largely driven by LOS and per diem cost, while average self-reported LOS in the MCBS was significantly lower than the average LOS recorded in the VHA PTF in calendar years 1998 and 2000, it was higher in calendar years 1999, 2001, and 2002 (Table 7). Interestingly, CMS's average per diem cost estimates for either the amount paid by the VHA and/or the amount paid by all third-party payers were significantly higher than HERC's per diem costs (Table 7). Thus, neither of these two factors, LOS and/or per diem costs, appear to be driving the differences we observed between MCBS's and HERC's inpatient cost estimates.
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While the distribution of CMS's imputed cost estimates for VHA outpatient services appeared reasonably close to the HERC Outpatient Average Costs (Table 8), because respondents tended to underreport their healthcare utilization in the MCBS, average annual outpatient costs per person were lower in the MCBS dataset than estimates using NPCD utilization and HERC Average Cost estimates (Figures 2, 3, and 6).
Alternatively, even though prescriptions were also underreported in the MCBS datasets, CMS's imputed costs for VHA prescriptions were significantly higher than PBM costs (Table 8). This contributed to CMS's estimates of average annual prescription costs per person being substantially higher than prescription costs for the same group of veteran respondents in the PBM files for calendar years 1998 to 2002 (Figures 4 and 7).
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Since the item nonresponse rates were very low and resulted in a relatively small proportion of the MCBS veteran sample with missing data, generally speaking, this issue alone did not compromise the representativeness of the MCBS veteran sample. However, because we found significant differences in the demographic characteristics of the Medicare eligible samples of veterans in the MCBS and NSV, the representativeness of the two samples remains unclear. The analysis involved subsamples of survey respondents of both the MCBS (i.e., veterans) and the NSV (i.e., the elderly and disabled who are eligible for Medicare). Since the sampling methodologies for the two surveys differ, the two samples may not be directly comparable. However, the MCBS sampling strategy was specifically designed to ensure that the sample was representative of the Medicare population. Thus, the differences that we found are necessary but not sufficient to draw any conclusions regarding the representativeness of the MCBS sample of Medicare-eligible veterans.
Consistent with the literature on self-reported utilization, we found that VHA inpatient, outpatient, and pharmaceutical events in the MCBS dataset were underreported relative to the VHA administrative databases. Since the magnitude of the underreporting appears to be consistent over time, the results of studies focused solely on analyzing trends in VHA utilization using the MCBS self-reported VHA data should be valid. Although the data from the earlier time trends (calendar years 1992-1997) were not presented, the self-reported utilization data from these earlier years through 2002 were trending in the same direction.
Researchers interested in doing comparative research across Medicare and VHA sectors should be aware of the limitations associated with greater underreporting observed for VHA outpatient events. Much of these differences are likely due to systemic differences between the VHA and private systems of care, namely, financial incentives for patients to schedule multiple clinic stops during 1-day visits to VHA medical facilities.
In this study, we compared MCBS events with those in the VHA PTF, Event, Visit, and PBM files, and did not consider OPC provided under contract by non-VHA facilities. Underreporting issues may be further amplified in rural areas where the VHA has concentrated on improving access to care by establishing contracts with Community-Based Outpatient Clinics (CBOCs). If these CBOCs are under contract to provide services through the VHA, veterans may not recognize or report these events as VHA events. Researchers interested in tracking contracted healthcare events will likely find them in the VHA's Fee Basis Files and not in the VHA's Medical SAS outpatient files generated from the NPCD.
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A number of factors may help explain why the VHA's PBM prices are lower than CMS's imputed prescription costs. First, since the VHA is able to negotiate pharmacy formulary discounts, the VHA typically pays lower prices than Medicare for drugs commonly used by seniors. A study conducted by Families USA found a median difference of 46 percent between the VHA price and the lowest price paid by Medicare drug plans . Second, although PBM costs reflect the direct cost of drugs paid by the VHA, the PBM costs do not include overhead or dispensing costs. Since the VHA's Decision Support System (DSS) dataset allocates indirect costs among VHA prescriptions, a comparison of DSS and PBM prescription costs found that indirect costs accounted for as much as 27.7 percent of the average cost of a VHA outpatient prescription in FY2002 . Since CMS's imputed VHA prescription costs are likely based on prices realized by Medicare/non-VHA sectors of the healthcare market, the combination of the two factors described above, PBM's nonaccounting for indirect costs, and CMS's nonaccounting for the VHA's formulary discounts would have contributed to the discrepancies in prescription costs we observed.
Although discrepancies in hospitalization costs were partially attributable to differences between self-reported LOS and LOS recorded in the VHA PTF, the majority of these differences are likely due to differences in CMS's and HERC's costing methodologies. In some years, as many as 75 percent of the VHA inpatient stays in the MCBS had split the cost of the stay between the VHA and the patient, attributing half of the cost of the stay as the patient's out-of-pocket expense. Although the VHA is allowed to bill veterans' private insurance for VHA services, they are not allowed to bill Medicare, nor is the VHA allowed to bill a veteran for the portion of the bill not covered by private insurance. In addition, for non-SC veterans, the VHA's copayments for hospitalizations are similar to Medicare's . Splitting VHA hospitalization costs 50/50 with the patient and/or Medicare is not typical VA policy. Thus, we believe that CMS's inpatient costs lack the accuracy that VA researchers have come to appreciate when using VHA costing databases such as those developed by HERC.
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This brings us to one of the more salient issues faced by policymakers: the relative proportion of the Federal budget spent on caring for elderly veterans through the VHA and Medicare. With more than a quarter of the Medicare population comprised of veterans as those proportions rise with the next generation of aging (Vietnam war) veterans, policies affecting access to VHA and Medicare services will affect utilization levels and thus both the VHA's and Medicare's budgets.
Since the MCBS consists of a nationally representative sample of Medicare-eligible veterans, the MCBS dataset is uniquely suited for analyzing the effect of changes in program eligibility, the effect of the expansion of medical facilities on access to care, and potential quality improvements associated with comanaging care provided by multiple providers and programs. In particular, changes in Medicare benefits may also lead to changes in the availability of supplemental coverage from former employers that, in turn, could affect decisions regarding where to seek care . Because veterans consider the VHA an important source of coverage for prescription drugs, analyzing the effect of Medicare Part D on the use of VHA prescription services, for example, would inform the VHA of the potential effect on the VHA's (and Medicare's) medical expenditures.
Since the MCBS conducts a careful reconciliation process comparing self-reported utilization and cost data to Medicare FFS administrative claims data, the accuracy associated with the utilization and cost of Medicare FFS health services is ensured. We found that aside from the issue of under-reporting typically associated with self-reported data, CMS's imputed cost estimates suffer from substantial methodological issues and measurement error. Although CMS has tried to impute the amount paid by various payers for VHA events reported in the MCBS dataset, eligibility for VHA services and the mix of (private) payers the VHA is able to bill for VHA services, including patient copayments, is complex and not easily simulated. Not only is the scope of VHA healthcare benefits fairly complex, but the VHA has undergone significant administrative and eligibility reforms and has greatly evolved over time.
If the costs of VHA services, including inpatient hospitalizations, are critical to addressing study objectives, since inpatient stays are relatively expensive and can significantly influence study results, researchers should not rely on the MCBS's imputed VHA inpatient cost estimates. Reliable VHA utilization and cost data are available for all types of care (inpatient and outpatient care from FY1998 onward and prescriptions from FY1999 onward). Thus, for studies focusing solely on these later time periods, researchers with access to VHA datasets should merge these data into the MCBS. Since the methodology HERC used to estimate the value of VHA resources is based on Medicare relative value units, HERC's cost estimates for VHA services are comparable with Medicare cost estimates. While HERC cost estimates do not consider the mix of potential payers or the amount of out-of-pocket expenses veterans are responsible for, they do reflect the value of healthcare resources expended on behalf of caring for veterans. Comparisons of programmatic spending on the Medicare population would be greatly validated by merging in VHA cost estimates that accurately represent the VHA's dedication to improving, as well as maintaining, the health of this potentially vulnerable elderly veteran population.
Abbreviations: CBOC = Community-Based Outpatient Clinic, CMS = Centers for Medicare and Medicaid Services, CPT = Current Procedure and Terminology, DSS = Decision Support System, FFS = Fee-For-Service, FY = fiscal year, HERC = Health Economics Resource Center, LOS = length of stay, MCBS = Medicare Current Beneficiary Survey, NPCD = National Patient Care Database, NSV = National Survey of Veterans, OPC = outpatient care, PBM = Pharmacy Benefits Management, PSSG = Planning System Support Group, PTF = Patient Treatment File, SC = service connected, VA = Department of Veterans Affairs, VHA = Veterans Health Administration.
Study concept and design: Y. Jonk, R. Feldman, D. C. Ripley, B. Dowd.
Acquisition of data: Y. Jonk, H. O'Connor, T. Schult, A. Cutting, D. C. Ripley.
Analysis and interpretation of data: Y. Jonk, H. O'Connor, T. Schult, R. Feldman, D. C. Ripley, B. Dowd.
Drafting of manuscript: Y. Jonk, R. Feldman, D. C. Ripley, B. Dowd. Critical revision of manuscript for important intellectual content: Y. Jonk, R. Feldman, D. C. Ripley, B. Dowd.
Statistical analysis: Y. Jonk, H. O'Connor, T. Schult, R. Feldman, D. C. Ripley, B. Dowd.
Obtained funding: Y. Jonk, R. Feldman, D. C. Ripley, B. Dowd.
Administrative, technical, or material support: Y. Jonk, A. Cutting. Study supervision: Y. Jonk.
Financial Disclosures: The authors have declared that no competing interests exist.
Funding/Support: This material is based on work supported by VA Health Services Research and Development Service (grant IIR 01-164) and presented, in part, at the VA HERC Cyber Seminar Series, April 2007.
Additional Contributions: The opinions are the authors' and do not reflect those of the VA, the VHA, or VA Health Services Research and Development Service. The authors would like to thank Todd Wagner, PhD, Health Economist at HERC, for reviewing the tables comparing HERC average cost estimates and CMS imputed costs in the MCBS.
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Submitted for publication October 26, 2009. Accepted in revised form May 13, 2010.
This article and any supplementary material should be cited as follows: Jonk Y, O'Connor H, Schult T, Cutting A, Feldman R, Ripley DC, Dowd B. Using the Medicare Current Beneficiary Survey to conduct research on Medicare-eligible veterans. J Rehabil Res Dev. 2010;47(8):797-814.
Yvonne Jonk, PhD; (1) * Heidi O'Connor, MS; (1) Tamara Schult, MPH; (2) Andrea Cutting, MA; (2) Roger Feldman, PhD; (3) Diane Cowper Ripley, PhD; (4) Bryan Dowd, PhD (3)
(1) Rural Health Research Center, Division of Health Policy and Management, School of Public Health, University of Minnesota, Twin Cities Campus, Minneapolis, MN; (2) Center for Chronic Disease Outcomes Research, Minneapolis Department of Veterans Affairs Medical Center, Minneapolis, MN; (3) Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN; (4) Health Services Research and Development/ Rehabilitation Research and Development Rehabilitation Outcomes Research Center, North Florida/South Georgia Veterans Health System, Gainesville Division, Gainesville, FL
* Address all correspondence to Yvonne Jonk, PhD; University of Minnesota-Rural Health Research Center, 2520 University Avenue SE, Suite 201, Minneapolis, MN 55414; 612-623-8312; fax: 612-623-8324. Email: firstname.lastname@example.org
** Because of the MCBS's complex design, researchers need to weight the sample so that estimates of population totals, percentages, means, ratios, and counts of persons and events are representative and generalizable to the entire Medicare-eligible population. The weights reflect the overall selection probability of each sample person and include adjustments for survey nonresponse and stratified sampling design based on age, sex, race, region, and metropolitan area. CMS provides documentation and tables of weighted results that researchers can use to ensure that their statistical packages and programming methodologies are handling the weights correctly. We conducted all analyses using STATA v10 (Strata Statistical Software; College Station, Texas).
*** Researchers with access to VA data can obtain lists of current VHA medical facilities from the VHA's Site Tracking System updated and maintained by the VA Planning System Support Group (PSSG), a field unit of the Assistant Deputy Under Secretary for Health for Policy and Planning. Available upon request, the PSSG has a historical VA facility file for FY2002 onward containing VA facility codes, addresses, zip codes, and latitude and longitude coordinates. The distance between the patient's home address and zip code and the nearest VA facility can be calculated using a recently developed distance function available in SAS V9.2 (SAS Institute Inc; Cary, North Carolina).
Table 1. Medicare Current Beneficiary Survey (MCBS) veteran sample size by Veterans Health Administration (VHA) use, 1992 to 2002. Veterans VHA Users Calendar Year Person Years Percent * Person Years Percent 1992 2,181 7.7 274 10.6 1993 2,368 8.3 276 10.3 1994 2,516 8.8 298 10.4 1995 2,357 8.3 283 10.8 1996 2,360 8.3 274 10.7 1997 2,576 9.0 313 12.0 1998 2,746 9.6 343 12.2 1999 2,879 10.1 387 13.6 2000 2,835 10.0 493 16.6 2001 2,864 10.1 599 20.3 2002 2,824 9.9 697 24.4 Total 28,506 100.0 4,237 100.0 Notes: Sample consists of noninstitutionalized veterans in MCBS Cost and Use files. Since respondents are followed for 4 years, 28,506 person years of data translates into data on 11,121 unique veterans. VHA users are defined as anyone who has used any type of VHA service, including inpatient, outpatient, and/or prescription services. Percentages of VHA users have been weighted so that they are representative of general veteran Medicare population. * Percentages may not add up to 100 due to rounding. Table 2. Data sources available for validating Medicare Current Beneficiary Survey's (MCBS's) self-reported utilization and imputed cost estimates. Sector Type of Care Utilization Data Cost Data Medicare Inpatient Medicare FFS Medicare FFS FFS Outpatient Administrative Administrative Pharmacy Claims Claims VHA Inpatient PTF Pre-FY1998: CDR Post-FY1998: HERC Inpatient AC Post-FY1999: DSS NDE Outpatient Pre-FY1997: OPC Pre-FY1998: CDR Post-FY1998: HERC Outpatient AC Post-FY1997: NPCD Post-FY1999: DSS NDE Pharmacy Pre-FY1999: Pre-FY1999: Not Not Available Available Post-FY1999: PBM Post-FY1999: PBM Post-FY2002: DSS NDE Post-FY2002: DSS NDE Note: Although DSS NDE files were not used in this study, they are a viable alternative source of VHA cost data. AC = average cost, CDR = Cost Distribution Report, DSS = Decision Support System, FFS = Fee-For-Service, FY = fiscal year, HERC = Health Economics Resource Center, NDE = National Data Extracts, NPCD = National Patient Care Database, OPC = outpatient care, PBM = Pharmacy Benefits Management, PTF = Patient Treatment File, VHA = Veterans Health Administration. Table 3. National Survey of Veterans (NSV) and Medicare Current Beneficiary Survey (MCBS) sample characteristics, 1992 and 2001. Characteristic 1992 NSV MCBS Sample Size (person years) 5,114 2,181 Male (%) 96.9 96.7 Age (%)* 18-44 years 1.4 1.1 45-64 years 5.9 6.1 [greater than or equal to] 92.6 92.7 65 years White (%) 91.3 91.5 Hispanic (any race, %) 2.9 2.8 Education (%)* Less than High School 30.3 35.5 ([dagger]) High School Graduate (only) 25.4 30.1 ([dagger]) Some College 24.7 15.3 ([dagger]) College Graduate 19.5 19.2 Marital Status (%) * Never Married 3.6 3.9 Married 80.4 76.4 ([dagger]) Divorced/Separated 6.3 8.3 ([dagger]) Widowed 9.7 11.4 ([dagger]) Health Status (%) * Excellent 15.8 19.8 ([dagger]) Very Good 22.3 25.4 ([dagger]) Good 28.5 29.4 Fair 19.3 16.7 ([dagger]) Poor 14.1 8.7 ([dagger]) Have SC Rating? (Yes, %) 13.6 14.4 SC Rating (%) * 1-25 49.9 53.7 ([dagger]) 26-50 29.1 20.0 ([dagger]) 51-75 6.9 10.9 ([dagger]) 76-100 14.2 15.3 Medicaid Coverage (%) 2.4 2.0 VHA Service Use Last Year (%) Inpatient 2.7 2.4 ([dagger]) Outpatient 7.4 9.4 ([dagger]) Prescription NA 7.8 Characteristic 2001 NSV MCBS Sample Size (person years) 9,217 2,864 Male (%) 96.9 96.9 Age (%)* 18-44 years 1.3 0.8 ([dagger]) 45-64 years 7.0 6.2 [greater than or equal to] 91.7 93.0 ([dagger]) 65 years White (%) 91.2 91.5 Hispanic (any race, %) 1.2 3.8 * Education (%)* Less than High School 20.4 24.0 High School Graduate (only) 29.5 27.1 Some College 27.4 26.1 College Graduate 22.7 22.8 Marital Status (%) * Never Married 3.6 3.7 Married 76.7 73.6 ([dagger]) Divorced/Separated 8.4 9.1 Widowed 11.3 13.6 ([dagger]) Health Status (%) * Excellent 10.7 15.9 ([dagger]) Very Good 22.0 30.0 ([dagger]) Good 31.5 30.8 Fair 22.6 15.6 ([dagger]) Poor 13.2 7.6 ([dagger]) Have SC Rating? (Yes, %) 13.9 10.3 ([dagger]) SC Rating (%) * 1-25 48.0 50.2 ([dagger]) 26-50 24.0 21.2 ([dagger]) 51-75 8.8 11.6 ([dagger]) 76-100 19.2 17.0 ([dagger]) Medicaid Coverage (%) 7.2 3.6 ([dagger]) VHA Service Use Last Year (%) Inpatient 3.3 1.5 ([dagger]) Outpatient 21.7 13.7 ([dagger]) Prescription 16.6 19.1 ([dagger]) * Percentages may not add up to 100 due to rounding. ([dagger]) Differences are significant at p < 0.05. NA = not available, SC = service connected, VHA = Veterans Health Administration. Table 4. Percent of veterans with Veterans Health Administration (VHA) events in Medicare Current Beneficiary Survey (MCBS) and VHA administrative databases, calendar years 1998 to 2002. Data shown as percent (n in person years) 1998 1999 Event VHA MCBS VHA MCBS Data Data Data Data Total 2,746 2, 879 Inpatient 1.6 (43) 1.4 (38) 1.9 (55) 1.6 (47) Outpatient 13.9 (381) 10.7 * (295) 14.9 (430) 10.6 * (305) Prescriptions NA NA 13.4 (387) 11.6 * (333) 2000 2001 Event VHA MCBS VHA MCBS Data Data Data Data Total 2,8 ;35 2, 64 Inpatient 1.8 (52) 1.7 (47) 1.6 (47) 1.4 (40) Outpatient 17.4 (494) 13.0 * (369) 22.2 (635) 13.8 * (396) Prescriptions 16.2 (458) 16.1 (457) 20.6 (589) 19.7 (564) 2002 Event VHA MCBS Data Data Total 2,824 Inpatient 1.5 (41) 1.5 (43) Outpatient 24.6 (694) 17.2 * (486) Prescriptions 23.1 (652) 23.1 (651) Note: VHA administrative databases include inpatient Patient Treatment File, National Patient Care Database, and Pharmacy Benefits Management database. * Differences are significant at p < 0.05. NA = not available. Table 5. Medicare Current Beneficiary Survey (MCBS) underreporting rates: percentage of events that were not reported by MCBS respondents, 1998 to 2002. Inpatient Stays Outpatient Events Calendar Year Medicare Stays VHA Stays * Medicare Events (%) (%) 1998 11.7 -9.1 45.2 1999 12.1 8.9 41.5 2000 16.7 17.2 42.8 2001 17.0 14.0 46.1 2002 17.2 9.4 45.6 Outpatient Events Calendar Year VHA Day Visits * VHA Clinic Stops * (%) 1998 53.3 67.0 1999 57.4 71.0 2000 66.7 76.0 2001 63.4 73.7 2002 64.8 74.9 Note: Self-reported events in MCBS were compared with Medicare Fee- For-Service and VHA administrative records. Higher percentages imply that more events were not reported, i.e., more events were underreported. Negative percents imply that patients reported more events than were recorded in administrative databases, i.e., they reported using more services than they actually did. * Differences are significant at p < 0.05. VHA = Veterans Health Administration. Table 6. Average annual Veterans Health Administration (VHA) inpatient costs (in dollars), number of inpatient days, and number of inpatient stays per person using VHA and Medicare Current Beneficiary Survey (MCBS) data, 1998 to 2002. By Person VHA Data Calendar Sample HERC Average Cost Inpatient Days Year Size (mean [+ or -] SD) (mean [+ or -] SD) 1998 24 17,301 [+ or -] 23,551 17.5 [+ or -] 28.0 1999 32 17,759 [+ or -] 27,000 18.3 [+ or -] 31.3 2000 35 22,775 [+ or -] 27,606 30.5 [+ or -] 56.8 2001 31 21,671 [+ or -] 26,437 14.2 [+ or -] 20.3 2002 22 16,609 [+ or -] 22,236 12.5 [+ or -] 19.1 By Person VHA Data MCBS Data Calendar Sample Inpatient Stays CMS Imputed VHA Cost Year Size (mean [+ or -] SD) (mean [+ or -] SD) 1998 24 1.5 [+ or -] 0.9 4,210 [+ or -] 3,131 1999 32 1.7 [+ or -] 1.5 5,368 [+ or -] 5,820 2000 35 2.2 [+ or -] 2.6 8,037 [+ or -] 10,141 2001 31 1.6 [+ or -] 1.3 5,205 [+ or -] 5,197 2002 22 1.5 [+ or -] 0.8 5,573 [+ or -] 5,565 By Person MCBS Data Calendar Sample Inpatient Days Inpatient Stays Year Size (mean [+ or -] SD) (mean [+ or -] SD) 1998 24 10.3 [+ or -] 10.0 1.6 [+ or -] 1.2 1999 32 18.2 [+ or -] 23.4 1.4 [+ or -] 0.9 2000 35 10.7 [+ or -] 11.8 1.5 [+ or -] 0.8 2001 31 18.8 [+ or -] 33.4 1.5 [+ or -] 1.2 2002 22 12.4 [+ or -] 20.2 1.3 [+ or -] 0.6 Note: Differences in average annual costs per person are attributable to differences between CMS and HERC costing methodologies and to measurement and recall errors. CMS = Centers for Medicare and Medicaid Services, HERC = Health Economics Resource Center, SD = standard deviation. Table 7. Medicare Current Beneficiary Survey (MCBS) and Health Economics Resource Center (HERC) inpatient cost estimates (in dollars): differences in event costs, length of stay (LOS), and third-party payer reimbursements, 1998 to 2002. By Event VHA Data No. of HERC LOS Year Inpatient Cost (mean [+ or -] SD) Stays (mean [+ or -] SD) 1998 16 11,863 [+ or -] 10,295 12.0 [+ or -] 10.6 1999 19 11,208 [+ or -] 12,935 11.0 [+ or -] 13.5 2000 34 10,219 [+ or -] 16,732 13.7 [+ or -] 28.2 2001 29 12,331 [+ or -] 18,484 10.7 [+ or -] 19.2 2002 20 11,419 [+ or -] 16,404 8.6 [+ or -] 15.2 MCBS Data Event Cost Covered By Event VHA Data by All Payers No. of Per Diem Cost LOS Year Inpatient (mean [+ or -] SD) (mean [+ or -] SD) Stays 1998 16 1,154 [+ or -] 851 7.8 [+ or -] 10.3 1999 19 1,247 [+ or -] 1,035 12.5 [+ or -] 17.6 2000 34 1,123 [+ or -] 718 6.3 [+ or -] 9.4 2001 29 1,722 [+ or -] 1,171 13.4 [+ or -] 19.9 2002 20 1,695 [+ or -] 986 9.7 [+ or -] 19.8 MCBS Data Event Cost Covered By Event by All Payers No. of Imputed Per Diem Cost Year Inpatient Event Cost (mean [+ or -] SD) Stays (mean [+ or -] SD) 1998 16 5,867 [+ or -] 5,328 3,396 [+ or -] 6,021 1999 19 7,663 [+ or -] 8,709 2,777 [+ or -] 5,217 2000 34 7,168 [+ or -] 5,939 3,356 [+ or -] 3,848 2001 29 6,160 [+ or -] 3,597 1,601 [+ or -] 1,556 2002 20 8,920 [+ or -] 8,747 2,862 [+ or -] 3,864 MCBS Data By Event Event Cost Covered by VHA No. of Imputed Per Diem Cost Year Inpatient VHA Cost (mean [+ or -] SD) Stays (mean [+ or -] SD) 1998 16 4,216 [+ or -] 3,406 1,289 [+ or -] 1,319 1999 19 5,718 [+ or -] 6,161 1,472 [+ or -] 2,597 2000 34 8,578 [+ or -] 9,762 3,907 [+ or -] 5,373 2001 29 4,949 [+ or -] 4,960 1,272 [+ or -] 1,790 2002 20 5,800 [+ or -] 5,283 1,827 [+ or -] 2,086 Note: VHA inpatient stays in MCBS and VHA administrative datasets were matched by person and admission date. Since distribution of costs and LOS are not (typically) normally distributed, average per diem costs may not equal average total cost of stay divided by average LOS. MCBS data allocate cost of a healthcare event across each responsible third-party payer (i.e., insurance policy or program), while HERC's average cost datasets reflect 100% of cost of VHA resources consumed during the VHA stay, regardless of payers involved. Thus, differences in event-level costs are attributable to differences in LOS, per diem costs, and costing methodologies. SD = standard deviation, VHA = Veterans Health Administration. Table 8. Distribution of Veterans Health Administration (VHA) and Medicare Current Beneficiary Survey (MCBS) outpatient and prescription costs per event, 1998 to 2002. Cost Year 1998 1999 VHA: HERC Outpatient Average Cost No. of Events 6,033 6,439 Mean [+ or -] SD 93.8 [+ or -] 156.0 101.6 [+ or -] 222.1 ($/Event) Median ($/Event) 61.7 68.0 MCBS: CMS Imputed Outpatient Costs No. of Events 1,751 1,750 Mean [+ or -] SD 121.0 [+ or -] 339.6 72.5 [+ or -] 236.1 ($/Event) Median ($/Event) 15.3 13.4 VHA: PBM Prescription Costs No. of Events NA 11,634 Mean [+ or -] SD NA 18.4 [+ or -] 37.6 ($/Event) Median ($/Event) NA 4.8 MCBS: CMS Imputed Prescription Costs No. of Events 5,650 7,724 Mean [+ or -] SD 42.8 [+ or -] 149.6 44.1 [+ or -] 81.2 ($/Event) Median ($/Event) 18.4 20.7 Cost Year 2000 2001 VHA: HERC Outpatient Average Cost No. of Events 7,017 7,222 Mean [+ or -] SD 102.3 [+ or -] 143.0 114.3 [+ or -] 166.8 ($/Event) Median ($/Event) 74.1 82.5 MCBS: CMS Imputed Outpatient Costs No. of Events 1,552 1,733 Mean [+ or -] SD 110.8 [+ or -] 318.8 97.3 [+ or -] 296.8 ($/Event) Median ($/Event) 28.6 24.5 VHA: PBM Prescription Costs No. of Events 15,289 18,284 Mean [+ or -] SD 21.3 [+ or -] 72.0 22.4 [+ or -] 67.3 ($/Event) Median ($/Event) 5.3 5.79 MCBS: CMS Imputed Prescription Costs No. of Events 9,109 10,761 Mean [+ or -] SD 51.4 [+ or -] 79.1 50.3 [+ or -] 75.5 ($/Event) Median ($/Event) 25.0 24.7 Cost Year 2002 VHA: HERC Outpatient Average Cost No. of Events 7,794 Mean [+ or -] SD 117.0 [+ or -] 181.4 ($/Event) Median ($/Event) 81.2 MCBS: CMS Imputed Outpatient Costs No. of Events 1,801 Mean [+ or -] SD 121.4 [+ or -] 741.0 ($/Event) Median ($/Event) 20.0 VHA: PBM Prescription Costs No. of Events 19,635 Mean [+ or -] SD 23.4 [+ or -] 63.2 ($/Event) Median ($/Event) 6.5 MCBS: CMS Imputed Prescription Costs No. of Events 5,650 Mean [+ or -] SD 42.8 [+ or -] 149.6 ($/Event) Median ($/Event) 18.4 Note: This comparison pertains to a group of veterans identified in both VHA and MCBS databases as having used VHA outpatient and prescription services, respectively. Number of events varies between VHA and MCBS data due to recall and measurement error. CMS = Centers for Medicare and Medicaid Services, HERC = Health Economics Resource Center, NA = not available, PBM = Pharmacy Benefits Management, SD = standard deviation.
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