Currently Viewing:
The American Journal of Managed Care March 2010
Ratio of Controller to Total Asthma Medications: Determinants of the Measure
Michael S. Broder, MD; Benjamin Gutierrez, PhD; Eunice Chang, PhD; David Meddis, PhD; and Michael Schatz, MD
An Intervention for VA Patients With Congestive Heart Failure
Laurel A. Copeland, PhD; Gregory D. Berg, PhD; Donna M. Johnson, MHS; and Richard L. Bauer, MD
Cost-Effectiveness of Pneumococcal Polysaccharide Vaccine Among Healthcare Workers During an Influenza Pandemic
Kenneth J. Smith, MD, MS; Mahlon Raymund, PhD; Mary Patricia Nowalk, PhD, RD;Mark S. Roberts, MD, MPP; and Richard K. Zimmerman, MD, MPH
Health Plan Use of Immunization Information Systems for Quality Measurement
Alan C. O'Connor, MBA; Christine M. Layton, PhD, MPH; Todd J. Osbeck, MM; Therese M. Hoyle; and Bobby Rasulnia, PhD
Improving Influenza Immunization in Pregnant Women and Healthcare Workers
Melanie E. Mouzoon, MD; Flor M. Munoz, MD; Anthony J. Greisinger, PhD; Brenda J. Brehm, MA; Oscar A. Wehmanen, MS; Frances A. Smith, MD; Julie A. Markee, RN, MPH; and W. Paul Glezen, MD
Mail-Order Versus Local Pharmacies on Adherence: Study Methods Make for Unfair Comparison
Todd A. Brown, MHP and Nathaniel M. Rickles, PhD. Reply by O. Kenrick Duru, MD, MSHS; Julie A. Schmittdiel, PhD; Wendy T. Dyer, MSc; Melissa M. Parker, MS; Connie S. Uratsu, MS; James Chan, PharmD; and Andrew J. Karter, PhD
Adoption and Use of Stand-Alone Electronic Prescribing in a Health Plan-Sponsored Initiative
Joshua M. Pevnick, MD, MSHS; Steven M. Asch, MD, MPH; John L. Adams, PhD; Soeren Mattke, MD, DSc; Mihir H. Patel, PharmD; Susan L. Ettner, PhD; and Douglas S. Bell, MD, PhD
Currently Reading
Risk Adjustment for Medicare Beneficiaries With Alzheimer's Disease and Related Dementias
Pei-Jung Lin, PhD; Matthew L. Maciejewski, PhD; John E. Paul, PhD;and Andrea K. Biddle, PhD
Inpatient Rehabilitation Utilization for Acute Stroke Under a Universal Health Insurance System
Hsuei-Chen Lee, PhD; Ku-Chou Chang, MD; Yu-Ching Huang, BS, RN; Chung-Fu Lan, DDS, DrPH; Jin-Jong Chen, MD, PhD; and Shun-Hwa Wei, PhD
Medical Care Costs Among Patients With Established Cardiovascular Disease
Gregory A. Nichols, PhD; Timothy J. Bell, MHA; Kathryn L. Pedula, MS; and Maureen O'Keeffe-Rosetti, MS
List of Peer Reviewers (2009)

Risk Adjustment for Medicare Beneficiaries With Alzheimer's Disease and Related Dementias

Pei-Jung Lin, PhD; Matthew L. Maciejewski, PhD; John E. Paul, PhD;and Andrea K. Biddle, PhD

Incorporating functional status in diagnosis-based risk adjustment measures may modestly improve overall expenditure prediction for beneficiaries with substantial disabilities, but not prescription cost prediction.

Objective: To compare prospective risk adjustment measures on their ability to predict expenditures for Medicare beneficiaries with Alzheimer’s disease and related dementias (ADRD).

 

Methods: Data were obtained from the 1999-2004 Medicare Current Beneficiary Survey linked with Medicare claims. Beneficiaries’ base-year demographic and health characteristics were used to construct risk adjustment measures, comorbidity measures, functional status measures, and prior expenditures that were used to predict the subsequent year’s total and drug expenditures. Adjusted R2 values, predictive ratios, and receiver operating characteristic curves were used to compare overall predictive power, accuracy of subgroup prediction, and accuracy in identifying beneficiaries with the top 10% of expenditures, respectively.

 

Results: The Centers for Medicare & Medicaid Services–Hierarchical Condition Category (CMS-HCC) and the Chronic Illness and Disability Payment System–Medicare had higher overall and subgroup predictive power for total expenditures compared with other diagnosis-based measures. The Prescription Drug Hierarchical Condition Category (RxHCC) exhibited greater predictive power for drug expenditures than other measures and outperformed other measures in identifying ADRD beneficiaries with extremely high drug expenditures. Adding functional status to single-measure models generally improved predictive power (ie, R2 value) for overall health expenditures by 2% to 4%, but not for drug expenditures.

 

Conclusions: The CMS-HCC and the RxHCC measures currently used by CMS are more predictive and accurate than other risk adjustment measures for overall and drug expenditure prediction for beneficiaries with substantial disabilities and comorbidities. Prediction of overall expenditures may be modestly improved for these beneficiaries by using a combined model of these measures and functional status.

 

(Am J Manag Care. 2010;16(3):191-198)

Overall expenditure prediction may be modestly improved for beneficiaries with substantial disabilities and comorbidities, such as those with Alzheimer’s disease and related dementias, by incorporating functional status.

 

  • Prior expenditures outperformed all other measures in the overall sample, the lower-cost group, and the higher-cost group, and can be used as a screening tool for high-cost case identification.
  • The Prescription Drug Hierarchical Condition Category measure currently used by Medicare is more predictive and accurate than other diagnosis-based risk adjustment measures for predicting prescription costs.
Until Medicare added diagnoses to risk adjustment methods (ie, including factors such as age, sex, county, institutional status, and Medicaid eligibility) to pay Medicare Advantage (MA) plans in 2000, policy makers and researchers were concerned that Medicare was overpaying these plans due to favorable selection of beneficiaries.1 The current Medicare capitation model, the Centers for Medicare & Medicaid version of the Diagnostic Cost Group–Hierarchical Condition Category (CMS-HCC),2 appears to address these historic concerns for the average beneficiary. However, risk-adjusted payment methods merit re-examination because the increasing prevalence of high-cost diseases coupled with increasing disability and frailty may worsen underprediction (and underpayment) for high-cost beneficiaries. For instance, the CMS-HCC may not adequately compensate health plans serving primarily disabled or frail populations.3-5 Underpayment creates disincentives for managed care plans to enroll beneficiaries with greater healthcare needs,5 which will not achieve the efficiency goals of the MA program.6

Many comparisons of risk adjustment and comorbidity measures are available for general populations, but there is much less examination of whether measures that perform best in general populations also perform best in disease-specific populations.7-10 In this analysis, we contrasted the CMS-HCC measure with others in predicting total health expenditures and drug expenditures among Medicare beneficiaries with Alzheimer’s disease and related dementias (ADRD) who have prominent functional disabilities. The CMS-HCC measure does not account for ADRD, thus providing a venue for testing its accuracy in comparison with other measures that account for dementia.

Survey-reported functional status, which is particularly important for chronically ill and frail populations, may complement claim-based diagnosis information to improve expenditure prediction for payment setting because functional status is not considered in claims-based measures.5 Therefore, we examined whether the addition of this frailty adjustment improved performance compared with single-measure models. We also examined the performance of these measures with respect to their broader use as a managerial tool for identifying a subgroup of frail, high-cost beneficiaries, which could inform efforts to target patients who may be amenable to medical management and cost-containment interventions.11,12

METHODS

Data Source and Sample

We analyzed data from the 1999-2004 Medicare Current Beneficiary Survey (MCBS) Cost and Use files, linked with Medicare Part A and Part B claims data.13 Information on prescription drug use was recorded from the survey. Elderly, community-dwelling beneficiaries (including those eligible for both Medicare and Medicaid), defined as adults age 65 years and older who were not institutionalized for more than 90 consecutive days during a year, were selected (N = 57,669). From these individuals, 2447 beneficiaries with 3606 person-year observations were identified as having ADRD, based on any of the following criteria14,15: (1) self or proxy report of ADRD; (2) the presence of any of the following diagnosis codes indicating ADRD in Medicare claims files16,17: all 290 codes, 291.2, 292.82, 294.1, 294.8, 331.0-331.2, and 797 defined by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes; or (3) use of any ADRDtargeted medications (ie, donepezil [Aricept], rivastigmine [Exelon], galantamine [Reminyl or Razadyne], memantine [Namenda]).

We then excluded 266 MA enrollees representing 438 person-year observations, because managed care plans were not required to submit claims with diagnoses to CMS in 1999-2004, to generate a sample of 2181 beneficiaries representing 3168 person-year observations. With the application of MCBS sampling weights, our sample was representative of 1.21 million to 1.91 million ADRD beneficiaries annually during 1999-2004, which is consistent with estimates from epidemiologic studies.18 We retained beneficiaries with less than full-payment year (ie, year t + 1) information to capture the potentially different expenditure pattern of decedents and nonrespondents.19,20 As a result, a 2-year panel of 1452 person-year observations representing 726 unique beneficiaries was analyzed, after excluding death or survey nonresponse during the base year t (n = 1455) and data for year t + 2 among beneficiaries who were observed in 3 years of MCBS data (n = 261).

Measures

Dependent Variables: Total Health Expenditures and Prescription Drug Expenditures. Six dependent variables were examined: (1) total healthcare expenditures, (2) prescription drug expenditures, (3) quartiles of total expenditures, (4) quartiles of drug expenditures, (5) top 10% of total expenditures, and (6) top 10% of drug expenditures. Total healthcare expenditures were defined in the MCBS as the sum of expenditures by Medicare, Medicaid, private insurance, out-of-pocket payments, and other sources across all types of services. Drug expenditures, a critical issue in managing ADRD,21 were defined as MCBS-imputed total payments for any prescription drugs paid by all sources.22 To assess whether higher- and lower-cost beneficiaries were under- or overpredicted, total and drug expenditures in year t + 1 were arranged in descending order and categorized into quartiles. We used a top-10% expenditure threshold and evaluated the measures’ ability to identify these high-cost beneficiaries. All expenditures were converted into constant 2007 dollars and were annualized by dividing them by the fraction of available person-days in a given year; all analyses were weighted by this fraction.19,20

Explanatory Variables: Risk Adjustment, Comorbidity, and Frailty Adjustment Measures

Centers for Medicare & Medicaid Services Hierarch ical Condition Category. We used the 2007 CMS-HCC measure, which was recalibrated using more recent data (ie, 2002-2003).23 Hierarchies are imposed among related Condition Categories (n = 189) to form 70 Hierarchical Condition Categories, to which “offered weights” (ie, payment weights provided by the software)24 are assigned. We calculated the CMS-HCC score for each beneficiary by summing the weights in the standard community model.

Chronic Illness and Disability Payment System–Medicare. We used the Chronic Illness and Disability Payment System25 Medicare version (CDPSM) with 16 major disease categories, which are divided further into 66 subcategories.26 Offered weights are assigned among subcategories to reflect the level of increased expenditures. The CDPSM score is the sum of the weights for all indicated subcategories, including a higher-cost subcategory of delirium and a lower-cost subcategory of dementia (see eAppendix available at www.ajmc.com for the complete list of ADRD-related ICD-9-CM diagnosis codes).

Prescription Drug Hierarch ical Condition Category. The Prescription Drug Hierarchical Condition Category (RxHCC) is a diagnosis-based model that CMS currently uses to adjust payments to Medicare prescription drug plans.19 Hierarchies are imposed among 197 Rx Condition Categories to create 89 RxHCCs. There  are 2 ADRD-related RxHCCs: dementia with depression or behavioral disturbance, and dementia/cerebral degeneration (eAppendix). The RxHCC score is the sum of  the offered weights for all indicated conditions.

Charlson Comorbidity Index. We used the Charlson Comorbidity Index (CCI) with the Deyo modification, which has 17 comorbidity categories including dementia (eAppendix).27,28 Each condition is assigned a weight of 1, 2, 3, or 6, reflecting the magnitude of the adjusted relative risks associated with each comorbidity. The CCI score is calculated as the sum of the offered weights for all indicated conditions.

Frailty Adjustment. Limitations in activity of daily living (ADL), a critical part of the ADRD progression, may exacerbate other chronic conditions; thus, ADRD beneficiaries incur persistently high expenditures over time.29,30 The frailty adjuster used here was the count of difficulty in performing ADLs, categorized as none, 1-2 (low), 3-4 (moderate), or 5-6 (high), as defined in the CMS frailty adjustment model.3

Prior Expenditures. Prior expenditures are highly correlated with expenditures in the following year.12,31 We used total healthcare expenditures in year t for total expenditure models, and drug expenditures in year t for drug expenditure models.

Combined Models. Beginning in 2004, ADL limitations were used concurrently with the CMS-HCC measure to adjust the payments made to selected organizations (eg, Program of All-Inclusive Care for the Elderly).3 We compared the performances of single-measure models and combined models by including ADL limitations into diagnosis-based models and prior-expenditure models. All models controlled for age (categorized as 65-69, 70-74, 75-79, 80-84, and 85+ years) and sex.

Analysis

Expenditures in prediction year t + 1 were regressed on each risk adjustment measure plus sex and age categories at year t using ordinary least squares (OLS)  regression. We also performed a sensitivity analysis using a generalized linear model (GLM) to assess whether alternative distributions changed the relative  ranking of measures’ predictive power,9 and used a modified Park test to determine the type of GLM to be used.32 We used adjusted R2 values from the OLS models and log likelihood values from the GLMs to assess overall prediction, with higher numbers indicating better model fit to estimate mean expenditures.

We computed predictive ratios (ie, predicted expenditures divided by actual expenditures) by quartile to assess the degree of overprediction or underprediction of expenditures in subgroups.31,33 Measures with less overprediction in upper quartiles and less underprediction in lower quartiles (indicated by predictive ratios closer to 1.0) are preferred. Then, we used receiver operating characteristic curves to identify the highest-spending 10% of the sample.11 A C statistic representing the area under the receiver operating characteristic curve was calculated. A value of 0.5 indicates no ability to discriminate; higher values between 0.5 and 1.0 indicate a better model fit.

RESULTS

Descriptive Statistics


The average age of our sample was 81.3 years and 60% were female (Table 1). The mean CMS-HCC, CDPSM, RxHCC, and CCI scores all were greater than 1.0, indicating a higher comorbidity burden than that observed in the general Medicare population. On average, beneficiaries with ADRD had 1.3 ADL limitations. Average total expenditures were $24,952 (in 2007 constant dollars) and average prescription drug expenditures were $2659 in year t + 1.

Overall Prediction

 
Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up