Medicare Capitation Model, Functional Status, and Multiple Comorbidities: Model Accuracy

October 15, 2008
Katia Noyes, PhD, MPH
Katia Noyes, PhD, MPH

Hangsheng Liu, PhD
Hangsheng Liu, PhD

Helena Temkin-Greener, PhD, MPH
Helena Temkin-Greener, PhD, MPH

Volume 14, Issue 10

This study of financial implications of risk adjustment for Medicare payments to individuals with comorbidities and functional impairment demonstrates the importance of controlling for disability.


: To examine financial implications of the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) risk-adjustment model on Medicare payments for individuals with comorbid chronic conditions.

Study Design

: The study used 1992-2000 data from the Medicare Current Beneficiary Survey and corresponding Medicare claims. Pairs of comorbidities were formed based on prior evidence about possible synergy between these conditions and activities of daily living (ADLs) deficiencies, and included heart disease and cancer, lung disease and cancer, stroke and hypertension, stroke and arthritis, congestive heart failure (CHF) and osteoporosis, diabetes and coronary artery disease, and CHF and dementia.


: For each beneficiary, we calculated the actual Medicare cost ratio as the ratio of the individual’s annualized costs to the mean annual Medicare cost for all people in the study. The actual Medicare cost ratios, by ADLs, were compared with HCC ratios under the CMS-HCC payment model. Using multivariate regression models, we tested whether having the identified pairs of comorbidities affected the accuracy of CMS-HCC model predictions.


: The CMS-HCC model underpredicted Medicare capitation payments for patients with hypertension, lung disease, CHF, and dementia. The difference between the actual costs and predicted payments was partially explained by beneficiary functional status and less-than-optimal adjustment for these chronic conditions.


: Information about beneficiary functional status should be incorporated in reimbursement models. Underpaying providers who care for populations with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions.

(Am J Manag Care. 2008;14(10):679-690)

Our findings indicate that information about beneficiary functional status should be incorporated in Medicare reimbursement models.

  • Underpaying providers who care for populations with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions.

Chronic conditions such as heart disease, hypertension, arthritis, cancer, and diabetes are the leading causes of disability and death in the United States for people older than 65 years.1 Medicare beneficiaries with 5 or more chronic conditions account for 68% of the program’s spending.2 Co-occurrence of diseases increases markedly with age, with two-thirds of noninstitutionalized Medicare beneficiaries over the age of 65 years reporting 2 or more chronic conditions,3 with the prevalence of multiple comorbidities being even higher among the Medicare population overall. Approximately 25% of those who experience chronic illness have some limitations in functional activity, and the percentage of those with disability increases with the number of coexisting conditions.4 The presence of chronic disease has been consistently shown to be associated with functional dependence,5-7 with combinations of diseases showing different influence on physical functioning than would be expected with the sum of the individual conditions.8-10

Recognizing the increasing prevalence of chronic comorbid conditions in the Medicare population, as well as the need to adequately compensate Medicare managed care plans for the care they provide to this segment of the population, beginning in 2004 the Centers for Medicare & Medicaid Services (CMS) started to phase in a new riskadjusted payment model. Known as the CMS-Hierarchical Condition Categories (CMS-HCC), this risk-adjustment model relies on demographic and diagnostic information available from administrative data to predict resource use. The model uses a selected subset of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9- CM) diagnostic codes from hospital and physician encounters to place beneficiaries into 70 disease groups: the HCCs.11,12 (The original model was developed using 1999-2000 claims. Starting in 2007, the HCC model was recalibrated using 2002-2003 data.) Each disease group includes conditions that are related clinically and have similar cost implications. In addition, the model accounts for the fact that having certain combinations of diseases may result in higher medical expenditures that simply are the sum of the 2 conditions. For instance, such disease interaction coefficients are allowed for diabetes and congestive heart failure (CHF); diabetes and cerebrovascular disease; diabetes, CHF, and renal failure; and a limited number of others.12

There have always been concerns regarding the accuracy of the HCC model in predicting Medicare payment.13-16 Understanding the relationship between functional limitations and cost of medical care in patients with multiple chronic conditions is currently gaining importance and recognition. In 2003, CMS created Special Needs Plans (SNPs), allowing healthcare providers to accept full risk from CMS for all medical and pharmacy health expenses for enrollees with specific chronic diseases.17 By the beginning of 2008, 775 plans enrolled nearly 1 million beneficiaries.18

The HCC model does not (except for the Program of All Inclusive Care for the Elderly [PACE]) include adjustment for functional impairment. Studies have shown that this lack of adjustment results in underestimation of payments for enrollees with disabilities. We also know that people with comorbid conditions tend to be more functionally disabled; hence, our interest in examining the extent to which the HCC model may not pay appropriately for this segment of the population, very significantly represented in the SNPs. Furthermore, the HCC model does not account at all for a number of prevalent chronic conditions (eg, dementia, osteoporosis); hence, our interest in including those conditions in the analyses.

The effect of multiple comorbidities on disability and cost of care is poorly understood. Ettinger and colleagues explored synergy between arthritis and 4 comorbidities (heart disease, pulmonary disease, obesity, and hypertension) and proposed a mechanism explaining increased disability resulting from multiple comorbidities.19 They suggested that an impairment from one disease (eg, inactivity resulting from arthritis) may exacerbate the impairment from another comorbid condition (eg, low work capacity caused by heart disease), thus modifying the disease-disability relationship. Prior studies also identified additional specific diseases such as cerebrovascular disorders, diabetes, cancer, osteoporosis, atherosclerosis, and neurologic problems that may exacerbate disability resulting from other conditions.8,7,10,19-23 Based on this evidence, we identified the following 11 target comorbidities to be examined in this study, taking into account the level of functional impairment: arthritis, hypertension, heart disease, cancer, lung disease, stroke, osteoporosis, diabetes, coronary artery disease (CAD), CHF, and dementia.7

Furthermore, we hypothesized that certain combinations of the 11 target conditions may have synergistic effects with respect to physical and cognitive functioning when evaluated longitudinally. This in turn would affect patient performance in activities of daily living (ADLs) and the subsequent cost of medical care. These predictions were based on the previous cross-sectional (arthritis and hypertension, heart disease and cancer, lung disease and cancer, and stroke and hypertension)10,24-26 and longitudinal7,8,16,21,27 studies. In addition, dementia may accelerate functional decline and mortality, and may exacerbate other chronic conditions as well. Osteoporosis could lead to more fractures and trauma in older patients, which would increase temporary and permanent disability and might limit people’s ADL performance as they tried to minimize their risks.

The purpose of this study is to assess the accuracy of the CMS-HCC Medicare capitation model in predicting Medicare expenditures for community-based beneficiaries with at least 2 target comorbidities identified above and various degrees of functional impairment. The population of Medicare beneficiaries with coexisting chronic conditions represents a good case for testing the accuracy of the CMS-HCC Medicare risk-adjustment model, which (1) does not account for patient functional limitations that may exacerbate the inaccuracy of predictions for patients with multiple chronic conditions and (2) does not account for all chronic conditions, which also may result in underestimation of payments.



Beneficiaries with the pairs of target conditions were compared with the general Medicare population on characteristics such as sex, race, frequency of each ADL, and place of residence using chi-square tests. Student t tests were used to identify significant differences between these groups of patients by age and number of ADLs. Survey weights were incorporated into the comparisons to represent the entire Medicare population. All statistical tests were 2 tailed and were performed using a significance level of 5%.

Comparing Actual Medicare Costs With the CMS-HCC Model Predictions

Using multiple regressions, we tested whether having the identified pairs of comorbidities affected the accuracy of CMSHCC model predictions. The dependent variable was the residual Medicare expenditures ratio, defined as the difference between the actual cost ratio and the predicted cost ratio (the HCC score) for each individual (similar to the approach used by Kautter and Pope13) and based on the work of Temkin-Greener and colleagues16 and Riley.15 The residual ratio reflects the accuracy of CMS-HCC model prediction. The independent variables included the dummy variables for the different levels of physical disability (ADLs), target comorbidities, and the interactions between these comorbidities. Survey sampling weights were incorporated in the multiple regression analysis. The analyses were conducted using STATA Statistical Software for Windows, Release 8.0 (College Station, TX: StataCorp) and SAS for Unix, Version 9 (Cary, NC: SAS Institute).


Table 1

Nearly three-quarters (72.55%) of all Medicare beneficiaries in our study had 2 or more target comorbidities, with the prevalence of different target comorbidities varying substantially. In , we compared the characteristics of the general Medicare population with those of beneficiaries who had pairs of target chronic conditions. Although more than a third of all beneficiaries had arthritis and hypertension, only about 1% of people had either CHF and osteoporosis or CHF and dementia. Patients with chronic illnesses were significantly older than the study population overall (age 72.75 years). Proportion of women was greater among patients with osteoporosis and CHF (87.88% women, P <.01), and arthritis and stroke (60.54%, women P <.05) or hypertension (66.44% women, P <.01) compared to the general Medicare population (57.56% women). Among patients with cancer and heart (45.98% men, P <.01) or lung disease (46.58% men, P <.01), diabetes and CAD (48.01% men, P <.01) proportion of men was greater compared to the general Medicare population (42.44% male). Except for the beneficiaries with cancer and heart disease, patients with the pairs of target comorbidities had lower income and were more likely to be on Medicaid than the general Medicare population.

Functional Status of Medicare Beneficiaries With Chronic Conditions


Patients with multiple comorbid conditions had a much greater level of ADL deficiencies than Medicare beneficiaries overall (). The profiles of disability also varied substantially between patients with different chronic illnesses. Patients with CHF and dementia reported the highest level of deficiency across all ADL categories: 14.38% relied on others&#8217; help with eating (feeding), and more than 50% used help or assisted devices for bathing. Other groups with a high ADL deficiency level included patients with stroke combined with hypertension or arthritis, CHF and osteoporosis, and CAD and diabetes. However, the ranking of the prevalence of individual ADLs was consistent among all patient groups, with eating being the least common and bathing being the most common function for which beneficiaries received help.

Table 2

Table 3

Comparing Actual Medicare Costs With CMS-HCC Predicted Payments Overall, the CMS-HCC model significantly underpredicted medical expenses of patients with target single comorbidities, except for arthritis (P = .13), cancer (P = .21), and osteoporosis (P = .32) ( and ). We found that for beneficiaries without functional limitations (0 ADLs), the CMS-HCC predicted expenses were no different from the actual cost ratios except for patients with CHF (underpredicted by 18.47%; P = .01). As the disability level increased, the model increasingly underpredicted the expenses&#8212;up to 43.65% (P <.001) for patients with 6 ADLs.

Table 4

Table 5

The discrepancy between the actual and predicted cost ratios was larger for beneficiaries with multiple comorbidities than for those with a single target condition. For example, the CMS-HCC model underpredicted the expenses of the beneficiaries with CHF and osteoporosis by 30.02% ( and ), while the predictions were 20.60% lower (P <.001) for patients with CHF only and no different from actual costs (P = .32) for osteoporosis only (Tables 2-3). The model also underpredicted medical expenses for the beneficiaries with arthritis and hypertension by 7.08% (P = .01), while underpredicting expenses by 5.70% for the patients with hypertension (P = .01) only; expenditures were underpredicted by 18.70% for patients with diabetes and CAD (P <.001), but only by 9.77% (P <.001) for patients with diabetes and 10.40% (P <.001) for patients with CAD. Moreover, the magnitude of the prediction error was greater for the pairs that included conditions without corresponding HCCs than for the conditions with corresponding HCCs (eg, CHF, cancer) or those accounted for by other HCCs (eg, hypertension, heart disease) (Tables 4-5).

The 95% CIs around the error estimates demonstrated that the study sample size was generally sufficient to make robust predictions. In some cases where the predicted error was not statistically significantly different from zero, the analysis of CIs illustrated clinically or practically substantial error (eg, for osteoporosis with 3 ADLs: P = .08; 95% CI = &#8722;5.69, 92.68; for lung disease and cancer with 1 ADL: P = .15; 95% CI = &#8722;7.37, 47.21).

Effect of Functional Status and Comorbidity on Medical Expenses

Because the majority of beneficiaries in our sample had more than 1 of the target comorbidities and various levels of functional impairment, we examined the joint impact of the multiple comorbidities and disability on the accuracy of the CMS-HCC capitation model (Table 3).

Table 6

Among the pairs of comorbid conditions, having arthritis and hypertension (0.079, P = .05), diabetes and CAD (0.260, P = .01), or CHF and dementia (0.783, P = .01) led to substantial underpayments as calculated by the CMS-HCC model. However, these differences were mainly due to the underpayment for the single conditions (CHF and dementia) rather than additional error due to having multiple comorbidities, because adding single conditions improved the explanatory power of the model (R2 = 0.34 compared with 0.21; ) and reduced the significance of P values (>.05) for the variables identifying pairs of conditions. Functional status helped explain even more of the difference between the actual costs and the predicted amount based on the capitation model (R2 = 0.46). The number of ADLs was highly significant (P < .01) in explaining the variation between actual costs and predicted payment, and so was the presence of hypertension, lung disease, and CHF (P <.05).


Our findings indicate that information about beneficiary functional status should be incorporated in Medicare reimbursement models because without functional-status adjustment such models are likely to underestimate costs of caring for patients with disability and multiple comorbidities. Underpaying providers who care for populations with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions.

Author Affiliations: From the Department of Community and Preventive Medicine, University of Rochester (KN, HL, HT-G), Rochester, NY; and RAND Corporation (HL), Pittsburgh, PA.

Funding Source: This publication was supported in part by a K01 AG 20980 grant from the National Institute on Aging (KN, HL). The use of the Medicare Current Beneficiary Survey was covered by the Data Use Agreement #12874. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health.

Author Disclosure: The authors (KN, HL, HT-G) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (KN, HL, HT-G); acquisition of data (KN, HT-G); analysis and interpretation of data (KN, HL, HT-G); drafting of the manuscript (KN, HT-G); critical revision of the manuscript for important intellectual content (KN, HL, HT-G); statistical analysis (KN, HL, HT-G); obtaining funding (KN); administrative, technical, or logistic support (KN); and supervision (KN).

Address correspondence to: Katia Noyes, PhD, MPH, Department of Community and Preventive Medicine, University of Rochester, 601 Elmwood Ave, Box 644, Rochester, NY 14620. E-mail:

1. Hoffman C, Rice D, Sung H. Persons with chronic conditions: their prevalence and costs. JAMA. 1996;276(18):1473-1479.2. Anderson GF. Medicare and chronic conditions. N Engl J Med. 2005;353(3):305-309.

4. Partnership for Solutions: Better Lives for People with Chronic Conditions. Chronic Conditions and Disability: Analysis of 2000 Census. June 2003. Accessed June 9, 2008.

6. Stuck A, Walthert J, Nikolaus T, Bula C, Hoffman C, Beck J. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Soc Sci Med. 1999;48(4):445-469.

8. Kriegsman D, Deeg D, Stalman W. Comorbidity of somatic chronic diseases and decline in physical functioning: the Longitudinal Aging Study Amsterdam. J Clin Epidemiol. 2004;57(1):55-65.

10. Fried LP, Bandeen-Roche K, Kasper J, Guralnik JM. Association of comorbidity with disability in older women: the Women’s Health and Aging Study. J Clin Epidemiol. 1999;52(1):27-37.

12. Pope GC, Kautter J, Ellis RP, et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004;25(4):119-141.

14. McCall N, Khatutsky G, Smith K, Pope GC. Estimation of nonresponse bias in the Medicare FFS HOS. Health Care Financ Rev. 2004;25(4):27-41.

16. Temkin-Greener H, Meiners MR, Gruenberg L. PACE and the Medicare+Choice risk-adjusted payment model. Inquiry. 2001;38(1):69-72.

18. Center for Medicare Advocacy. Medicare Advantage Special Needs Plans: A Beneficiary Perspective; October 18, 2007; Washington, DC. Accessed June 9, 2008.

20. Fillenbaum G, Pieper C, Cohen H, Cornoni-Huntley JC, Guralnik JM. Comorbidity of five chronic health conditions in elderly community residents: determinants and impact on mortality. J Gerontol A Biol Sci Med Sci. 2000;55(2):M84-M89.

22. Newschaffer CJ, Bush TL, Penberthy LT. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data. J Clin Epidemiol. 1997;50(6):725-733.

24. Berenson RA, Bodenheimer T, Pham HH. Specialty-service lines: salvos in the new medical arms race. Health Aff (Millwood). 2006;25(5):w337-w343.

26. Verbrugge LM, Lepkowski JM, Imanka Y. Comorbidity and its impact on disability. Milbank Q. 1989;67(3-4):450-484.

Accessed June 10, 2008.

29. Eppig F, Chulis GS. Matching MCBS and Medicare data: the best of the both worlds. Health Care Financ Rev. 1997;18(3):211-229.

31. Barker WH, Mullooly JP, Getchell W. Changing incidence and survival for heart failure in a well-defined older population, 1970-1974 and 1990-1994. Circulation. 2006;113(6):799-805.

33. Centers for Medicare & Medicaid Services. Drug coverage claims data. Part D payment and risk adjustment. 2008. Accessed June 10, 2008.

35. Center for Medicare Advocacy. Medicare Advantage Private Fee-for-Service (PFFS) Plans: A Primer for Advocates. 2008. Accessed June 10, 2008.

37. Strickland D, Bertoni JM. Parkinson’s prevalence estimated by a state registry. Mov Disord. 2004;19(3):318-323.

39. Swarztrauber K, Anau J, Peters D. Identifying and distinguishing cases of Parkinsonism and Parkinson’s disease using ICD-9-CM codes and pharmacy data. Mov Disord. 2005;20(8):964-970.

41. Winkelmayer WC, Schneeweiss S, Mogun H, Patrick AR, Avorn J, Soloman DH. Identification of individuals with CKD from Medicare claims data: a validation study. Am J Kidney Dis. 2005;46(2):225-232.

43. Nattinger AB, Laud PW, Bajorunaite R, Sparapani RA, Freeman JL. An algorithm for the use of Medicare claims data to identify women with incident breast cancer. Health Serv Res. 2004;39(6, pt 1):1733-1749.

45. Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005;43(5):480-485.