The rise in spending among Medicare beneficiaries: The role of chronic disease prevalence and changes in treatment intensity. Increasing numbers of beneficiaries being treated for five or more conditions a year are driving Medicare spending upward

Health Affairs

Volumn 25, Issue 5, 2006, Pages

  Thorpe, Kenneth E ^a   Howard, David H ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGED; ARTICLE; CHRONIC DISEASE; CLASSIFICATION; DRUG UTILIZATION; ECONOMICS; HEALTH CARE COST; HEALTH SURVEY; HUMAN; MEDICARE; METABOLIC SYNDROME X; PRESCRIPTION; PREVALENCE; UNITED STATES; UTILIZATION REVIEW;

AGED; CHRONIC DISEASE; DRUG PRESCRIPTIONS; DRUG UTILIZATION; HEALTH EXPENDITURES; HEALTH SURVEYS; HUMANS; MEDICARE; METABOLIC SYNDROME X; PRESCRIPTIONS, DRUG; PREVALENCE; UNITED STATES;

EID: 33749339052     PISSN: 02782715     EISSN: 02782715     Source Type: Journal    
DOI: 10.1377/hlthaff.25.w378     Document Type: Article

References (34)

1. Data are derived from the Congressional Budget Office, "The Long-Term Budget Outlook," December 2005, http://www.cbo.gov/ftpdocs/69xx/ doc6982/12-15-LongTermOutlook.pdf (accessed 10 August 2006). This assumes that per capita Medicare spending rises 2.5 percentage points higher than per capita GDP. Over the past thirty-four years, per capita Medicare spending has increased 2.9 percentage points faster than per capita GDP.
2. The paper uses the shorthand "spending among Medicare beneficiaries" with the recognition that we are tracking total health care spending linked to Medicare beneficiaries regardless of the source of payment (out of pocket, Medicaid, supplemental coverage). We also examined trends in spending flowing only through the Medicare program as well; that analysis produced results similar to those presented in this paper.
3. The annual National Health Expenditure Accounts (NHEA) reports created by the actuaries at the Centers for Medicare and Medicaid Services represent the best example of this type of study by provider. Their most recent estimates can be found at C. Smith et al., "National Health Spending in 2004:Recent Slowdown Led by Prescription Drug Spending," Health Affairs 25, no. 1 (2006): 186-196. The CBO also decomposes the growth in spending into changes in enrollment and changes in spending per enrollee. The CBO estimates that over the past thirty-four years, growth in spending per beneficiary has accounted for approximately 82 percent of the overall rise in federal Medicare spending. CBO, "The Long-Term Budget Outlook," Box 1-3 (p. 6). However, other studies conducted by the CMS track total health care spending among Medicare beneficiaries regardless of the source of payment. Estimates that use the Medicare Current Beneficiary Survey (MCBS) can track total spending. Moreover, estimates of spending from the CMS by age (for instance, those age sixty-five and older) do track total spending. See, for example, CMS, "Age Estimates in the National Health Accounts: Definitions, Sources, and Methods," http://www.cms.hhs.gov/NationalHealthExpendData/downloads/age-methodology.pdf (accessed 7 May 2006).
4. The estimate that the Medicare program directly finances 50 percent of total Medicare spending is based on a study by AARP. See C. Caplan, "What Share of Beneficiaries' Total Health Care Costs Does Medicare Pay?" September 2002, http://assets.aarp.org/rgcenter/health/dd78_costs.pdf (accessed 13 July 2006).
5. This collection of studies can be found as part of a 2005 Health Affairs Web-Exclusive collection, "Health and Costs of the Future Elderly," available online at http://content.healthaffairs.org/cgi/content/full/hlthaff. w5.r1/DC2 (accessed 13 July 2006).
6. M.E. Chernew et al., "Disability and Health Care Spending among Medicare Beneficiaries," Health Affairs 24 (2005): W5-R42-W5-R52 (published online 26 September 2006; 10.1377/hlthaff.W5.R42).
7. J. Lubitz et al., "Health, Life Expectancy, and Health Care Spending among the Elderly," New England Journal of Medicine 349, no. 11 (2003): 1048-1055.
8. Chernew et al., "Disability and Health Care Spending."
9. K.E. Thorpe et al., "The Rising Prevalence of Treated Disease: Effects on Private Health Insurance Spending," Health Affairs 24 (2005): w317-w325 (published online 27 June 2006; 10.1377/hlthaff.w5.317.
10. Each of the factors listed would result in a rise in treated prevalence. For instance, as mortality rates decline, Medicare enrollment per year would rise. Although this would increase spending annual spending, work by Lubitz and colleagues show that total lifetime spending (after age age) is the same among those with and without a limitation on an activity of daily living. Lubitz et al., "Health, Life Expectancy, and Health Care Spending." Also see G.F. Joyce et al., "The Lifetime Burden of Chronic Disease among the Elderly," Health Affairs 24 (2005): W5-R18-W5-R29 (published online 26 September 2006; 10.1377/hlthaff.W5.R18). Several studies have examined the role of technology and innovation as amajor factor accounting for rising spending. For a recent example of this literature, see A.A. Okunade and V.N.R. Murthy, "Technology as a 'Major Driver' of Health Care Costs: A Cointegration Analysis of the Newhouse Conjecture," Journal of Health Economics 21, no. 1 (2002): 147-159.
11. E.S. Ford, W. Giles, and W. Dietz, "Prevalence of the Metabolic Syndrome among U.S. Adults: Findings from the Third National Health and Nutrition Examination Survey," Journal of the American Medical Association 287, no. 3 (2002): 356-359.
12. C.M. Alexander et al., "NCEP-Defined Metabolic Syndrome, Diabetes, and Prevalence of Coronary Heart Disease among NHANES III Participants Age Fifty Years and Older," Diabetes 52, no. 5 (2003): 1210-1214.
13. Since MEPS and NHANES do not survey institutionalized patients (such as nursing home patients), their spending is excluded from the analysis. Yet because spending on nursing home care among those age sixty-five and older rose at virtually the same average rate as overall health care spending (7.9 percent) relative to personal health care spending in that age group, the omission is not likely to affect the results. Data are from the CMS data set, as in Note 3. Detailed descriptions of the sample design and data collection methods for each survey are available on both the MEPS and NHANES Web sites, http://www.meps. ahrq.gov and http://www.cdc.gov/nchs/nhanes.htm.
14. Our results are not sensitive to this choice, although it does result in lower spending among Medicare beneficiaries than in other estimates. Total reported spending, however, is lower than found in the NHEA and the MCBS (see Note 3). NMES and MEPS do not include spending on institutionalized patients, which results in lower total spending per year in our analysis.
15. An overview of the CCS approach and the codes may be found at Healthcare Cost and Utilization Project, "Clinical Classifications Software (CCS) for ICD-9-CM Fact Sheet," 22 November 2005, http://www.hcup-us.ahrq.gov/ toolssoftware/ccs/ccsfactsheet.jsp (accessed 13 July 2006).
16. Thorpe et al., "The Rising Prevalence."
17. S.H. Zuvekas and J.W. Cohen, "AGuide to Comparing Health Care Expenditures in the 1996 MEPS to the 1987 NMES," Inquiry 39, no. 1 (2002): 76-86.
18. The values may be found at National Aeronautics and Space Administration, "Gross Domestic Product Deflator Inflation Calculator," 21 January 2005, http://www1.jsc.nasa.gov/bu2/inflateGDP.html (accessed 13 July 2006).
19. We also conducted the analysis limiting Medicare beneficiaries to those age sixty-five and older. The results presented in this paper are not sensitive to the decision to include all Medicare beneficiaries.
20. 1987) We attributed the third term to changes in cost per case and treated prevalence based on the relative magnitudes of the first two terms. So, for example, we calculated the impact of the change in costs per case on the change in per capita costs by adding the first term to the product of (1) the third term and (2) the ratio of the first term to the sumof the first and second terms. The impact of the change in treated prevalence is defined analogously. The estimates can also be presented as ranges, where the lower bound of the increase in per capita costs attributable to the change in treated prevalence is the second term in the expression above and the upper bound is the sum of the second and third terms. The proportion of the increase attributable to the change in costs per case ranges from the first term (lower bound) to the sum of the first and third terms (upper bound). The estimates presented in the exhibit are the midpoints of these ranges.
21. K.E. Thorpe, C.S. Florence, and P. Joski, "Which Medical Conditions Account for the Rise in Health Care Spending?" Health Affairs 23 (2004): w437-w445 (published online 25 August 2004; 10.1377/hlthaff.w4.437).
22. R.J. Anderson et al., "The Prevalence of Comorbid Depression in Adultswith Diabetes: A Meta-Analysis," Diabetes Care 24, no. 6 (2001): 1069-1078.
23. Medical treatment means the use of any medical service, such as a physician visit, drug, hospitalization, or other ambulatory care visit associated with the medical condition. For each service, beneficiaries were asked about the medical condition leading to the use of service.
24. E. Ferrannini et al., "Hyperinsulinaemia: The Key Feature of a Cardiovascular and Metabolic Syndrome," Diabetologia 34, no. 6 (1991): 416-422.
25. NMES and MEPS rely on respondents' reporting their height and weight to those conducting the survey. In contrast, NHANES relies on actual clinical measures (height, weight, blood pressure) collected by physicians and other health care providers.
26. We also followed the methods outlined in K.E. Thorpe et al., "The Impact of Obesity on Rising Medical Spending," Health Affairs 23 (2004): w480-w486 (published online 20 October 2004; 10.1377/hlthaff.w4.480), to measure the share of the rise in spending that is linked to a rise in obesity. The two-part regression model included the same covariates in this work, but it also interacted obesitywith disability status. This method indicates that the rise in obesity among Medicare beneficiaries accounted for approximately 20 percent of the rise in spending during the period. These results are similar, although slightly higher, than the unadjusted accounting tabulation reported in the current paper.
27. The authors thank an anonymous reviewer of this paper for making this point.
28. E.W. Gregg et al., "Secular Trends in Cardiovascular Disease Risk Factors According to Body Mass Index in U.S. Adults," Journal of the American Medical Association 293, no. 15 (2005): 1868-1874.
29. Understanding the contribution of each of these factors to increases in treated prevalence is the subject of ongoing work. See D.H. Howard, S. Busch, and K.E. Thorpe, "Understanding Recent Trends in Disease Treatment Rates: Are We Getting Sicker or Getting Screened?" (Working paper, Emory University, 2006).
30. R. Berenson and J. Horvath, "Confronting the Barriers to Chronic Care Management in Medicare," Health Affairs 22 (2003): w37-w53 (published online 22 January 2003; 10.1377/hlthaff.w3.37).
31. See, for example, Gregg et al., "Secular Trends."
32. There is some evidence that the rise in spending on antihypertensive drugs has reduced the number of premature deaths from cardiovascular disease. This line of research estimates a health benefit to cost ratio of approximately 11:1. This literature indicates the higher spending may be cost effective. See, for example, G. Long et al., "The Impact of Antihypertensive Drugs on the Number and Risk of Death, Stroke and Myocardial Infarction in the United States," NBER Working Paper no. 12096 (Cambridge, Mass.: National Bureau of Economic Research, March 2006).
33. P. Zweifel, S. Felder, and M. Meiers, "Ageing of Population and Health Care Expenditures: A Red Herring?" Health Economics 8, no. 6 (1999): 485-496;
34. and J.F. Fries, "Measuring and Monitoring Success in Compressing Morbidity," Annals of Internal Medicine 139, no. 5, Part 2 (2003): 455-459.