Alarge body of evidence documents

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1 Is Spending More Always Wasteful? The Appropriateness Of Care And Outcomes Among Colorectal Cancer Patients An across-the-board reduction in services might eliminate valuable care in addition to reducing wasteful care. by Mary Beth Landrum, Ellen R. Meara, Amitabh Chandra, Edward Guadagnoli, and Nancy L. Keating ABSTRACT: Increased area-level medical spending is not correlated with improved patient outcomes or quality, thereby supporting the case for spending reductions in high-spending regions. However, all additional spending need not be wasteful. Examining the care of patients with colorectal cancer, we show that high-spending regions are more likely than other regions to use recommended care but are also more likely to use discretionary and nonrecommended care, the latter of which has adverse outcomes for patients. Our results show that instead of cutting spending, policies designed to target services to patients most likely to benefit could increase the value of medical spending. [Health Affairs 27, no. 1 (2008): /hlthaff ] Alarge body of evidence documents striking variation in Medicare spending per beneficiary across areas in the United States. 1 Most of this variation has been linked to geographic variation in practice patterns in particular, increased used of inpatient services, outpatient visits, diagnostic tests, and specialist visits. 2 Despite this greater use of health services, beneficiaries in high-spending areas are not more likely to receive high-quality care for some conditions, nor do they experience better health outcomes or satisfaction with care than those in low-spending areas. 3 These results have led many to conclude that the excess spending in high-spending areas stems primarily from overuse of discretionary care. This conclusion suggests that sizable cost savings could be achieved with no detriment to patient outcomes if practice patterns in high-spending areas mirrored those in lowspending areas. 4 However, equivalence of outcomes across areas does not necessary imply that excess spending is associated only with wasteful care. Another hypothesis is that high-spending areas provide more services, regardless of their appropriateness for specific patients. Higher rates of services overall could lead to equivalent outcomes across areas if increased use of Mary Beth Landrum (landrum@hcp.med.harvard.edu) is an associate professor of health care policy at Harvard Medical School in Boston, Massachusetts. Ellen Meara is an assistant professor of health care policy there. Amitabh Chandra is an assistant professor of public policy at Harvard s Kennedy School of Government. Edward Guadagnoli is a professor of health care policy at Harvard Medical School. Nancy Keating is an associate professor of medicine and of health care policy there. HEALTH AFFAIRS ~ Volume 27, Number DOI 1377/hlthaff Project HOPE The People-to-People Health Foundation, Inc.

2 Health Tracking recommended care in high-spending areas were offset by increased use of nonrecommended care that is harmful to patients. In this study we focus on a single condition, colorectal cancer, to better understand the relationship between spending, use of services, and patient outcomes. We studied colorectal cancer because it is prevalent, with more than 150,000 cases expected to be diagnosed in the United States in 2007; it is associated with high levels of spending; and effective treatment strategies for certain subsets of patients are known to be underused. 5 We examined whether greater service use among colorectal cancer patients in high-spending areas is concentrated among (1) recommended care that hasbeenlinkedtoimprovedpatientoutcomes; (2) discretionary care, in which there is either limited or mixed evidence on the relationship between care and patient outcomes; or (3) nonrecommended care, in which there is evidence or scientific rationale to believe that the care may be harmful. We also examined mortality as a function of area-level spending, to better understand why increased spending in an area does not lead to improved outcomes. Study Data And Methods Data source and type. We used the Surveillance, Epidemiology, and End Results (SEER) Medicare data. The SEER program of the National Cancer Institute collects uniformly reported data from eleven populationbased cancer registries covering approximately 14 percent of the U.S. population. Since 1991 the SEER data have been merged with Medicare administrative data by a matching algorithm that successfully links files for more than 94 percent of SEER registry patients diagnosed at age sixty-five or older. 6 Study cohorts. We selected patients with a first diagnosis of colorectal cancer during who were age sixty-six or older when diagnosed and continuously enrolled in traditional Medicare throughout the year before diagnosis. More recent diagnosis data are available from the SEER database, but we deliberately examined a cohort for whom it was possible to measure long-term survival. Varia- tionsincarealsohavebeenshowntobeper- sistent over time, which suggests that changes in treatment patterns across high- and lowspending areas are unlikely to bias our results. The final sample included 55,549 patients. 7 Measures of area-level spending. Following previous work on U.S. area variations in spending, we assigned patients to Dartmouth Atlas of Health Care Hospital Referral Regions (HRRs) based on their ZIP code of residence at the time of diagnosis. Using data on where Medicare patients were hospitalized for cardiovascular and neurosurgical care, the United States is divided into 306 HRRs. Patients in our cohort were living in one of 292 HRRs at the time of their diagnosis; 96 percent of patients lived in one of the forty-three HRRs contained within a SEER region. We categorized spending based on a publicly available measure of intensity of inpatient care at the end of life: the Dartmouth Atlas measure of price adjusted reimbursements for inpatient care during the last six months of life per Medicare decedent ( ), which we refer to as the end-of-life inpatient expenditure index (EOL-IEI). 8 Within areas, end-oflife spending is highly correlated with both total Medicare spending and spending in specific disease cohorts, including patients with colorectal cancer. 9 Examining the relationship between use of services, mortality, and arealevel spending using the EOL-IEI has several advantages over the use of spending on specific disease cohorts. The latter comparison would be affected by the severity of underlying disease. By focusing on variation in the treatment of patients with identical life expectancy (six months), the EOL-IEI better reflects the portion of area-level spending that is attributabletodifferencesinpracticepatternsasopposed to differences in severity of illness. Previous studies using a similar measure based on reimbursements for hospital and physician services in the last six months of life have shown little variation in burden of illness according to area-level spending at the end of life, providing empirical evidence that indices such as the EOL-IEI approximate random assignment of patients to different spending lev- 160 January/February 2008

3 els. 10 In addition, the EOL-IEI is adjusted to account for regional variation in prices using a geographic practice cost index. Appropriateness of care. To understand the relationship between spending and the use of recommended care, we first considered six recommended measures based on national consensus guidelines. We also examined the use of chemotherapy, a relatively expensive and potentially toxic therapy, in specific subsets of patients to understand treatment decision making in recommended, nonrecommended, and discretionary cases. We examined six measures based on guideline-recommended care for patients diagnosed with colorectal cancer. 11 Specifically, we assessed (1) stage at diagnosis as a measure of quality of screening services, classifying patients according to diagnosis with late (stage IV) versus early (stage I/II/III) disease; (2) adjuvant chemotherapy (chemotherapy providedinadditiontosurgery)forpatientswith stage III colon cancer; (3) adjuvant chemotherapy and radiation therapy for patients with stage II/III rectal cancer; (4) receipt of surveillance colonoscopy within one year after surgery for patients undergoing curative surgery; (5) complete diagnostic colonoscopy (colonoscopy prior to surgery or at the time of surgery); and (6) surveillance testing for the tumor marker carcinoembryonic antigen (CEA) for patients with stage II/III colorectal cancer. We examined the receipt of chemotherapy within six months of colon cancer diagnosis using Medicare administrative data. We analyzed receipt of chemotherapy according to stage of disease at diagnosis, which enabled us to examine the use of treatments when they are recommended (stage III; n = 11,261), not recommended (stage I; n = 10,998), or discretionary because the benefits and risks are less clear and vary by tumor characteristics and patients performance status (stage II and IV; n = 16,371 and 8,661, respectively). We also examined the proportion of patients who received chemotherapy stratified by age and comor- bidity score to assess whether higherspending areas tended to treat relatively older and sicker patients than lower-spending areas did. Because older and, in particular, sicker patientsmightbemoresusceptibletosometoxic effects of chemotherapy, and because their life expectancies are more limited, the benefit-torisk ratio is likely lower for this population than for other colorectal cancer patients. Mortality. We computed overall and colorectal cancer specific mortality at three years after diagnosis. 12 Characteristics of patients. We obtained information about age, race, Hispanic ethnicity, marital status, year of diagnosis, tumor stage, tumor grade, tumor size, and history of previous cancer from the SEER registry data. We used 1990 census data to obtain information on education and income by the census tract of residence. We measured comorbid illness with the Charlson index, using diagnostic information from both inpatient and ambulatory claims during the twelve-month period ending the month before diagnosis to best characterize patients comorbid diseases before diagnosis. 13 Analyses. We estimated logistic regression models to assess the adjusted effect of EOL-IEI on recommended care indicators, receipt of chemotherapy, and overall mortality, and we used multinomial regression models to assess the effect of EOL-IEI on cause-specific mortality (coded as alive, died from colorectal cancer, or died from other causes). For each model, the patient was the unit of analysis, with adjustment for individual-level covariates. The independent variables of interest were a set of four indicator variables designating the quintile of EOL-IEI in the HRR of residence at the time of diagnosis (using the lowest spending quintile as the reference group). 14 Models for chemotherapy and mortality were fit using all patients and in subsets of patients defined by stage at diagnosis. Toaidininterpretationofeachmodel sresults, we used regression coefficients to calculate adjusted average proportions of patients in each quintile of EOL-IEI receiving the service, holding all other covariates for each patientattheirindividualobservedvalue.we tested for trends according to EOL-IEI by refitting the models with EOL-IEI as a continu- HEALTH AFFAIRS ~ Volume 27, Number 1 161

4 Health Tracking ous variable and computing the adjusted change in probability associated with a $1,000 increase in EOL-IEI (almost equal to an increase of one quintile), holding all other covariates at their mean value. 15 Standard errors for the adjusted change in probability were estimated using a delta method approximation. Study Results Exhibit 1 displays characteristics of the cohort according to EOL-IEI quintile. 16 The EOL- IEI averaged over $12,000 in the highest quintile compared to approximately $7,500 in the lowest quintile. Patients living in high EOL-IEI areas were primarily reported by the Los Angeles and Detroit SEER registries, while patients living in low EOL-IEI areas were reported by the registries in Iowa, Seattle, or Utah. Patients living in areas with high levels of inpatient end-of-life spending were more likely to be nonwhite and live in areas with fewer high school graduates. Although average age did not vary across EOL-IEI quintiles, patients average level of comorbid disease was higher in the top EOL-IEI quintile. Recommended care. We found mixed EXHIBIT 1 Sociodemographic And Clinical Characteristics Of The Sample Of Medicare Patients With Colorectal Cancer Quintile of area-level spending Characteristic All patients $9,917) 1 $7,554) 2 $8,671) 3 $9,439) 4 $10,876) 5 $12,718) Number of patients Average age (years) Male Race a White b Black b Other b Hispanic ethnicity a,b 55, % 10, % 11, % 11, % 10, % 12, % 88.4% % % % % % Median household income in census tract of residence a,b $37,211 $30,371 $37,261 $39,847 $40,414 $38,103 Proportion of non high school graduates in census tract of residence a,b 20.9% 17.2% 19.1% 20.9% 20.2% 26.1% Charlson score = 0 b 64.9% 67.9% 68.5% 64.0% 64.2% 60.7% SEER region c San Francisco Connecticut Detroit Hawaii Iowa New Mexico Seattle Utah Atlanta San Jose Los Angeles 7.9% % % % % % SOURCE: Authors tabulations based on Surveillance, Epidemiology, and End Results (SEER) Medicare data. Average area spending measured using the end-of-life inpatient expenditure index (EOL-IEI). a Race was missing for percent of patients; Hispanic ethnicity was missing for 0.3 percent of patients; census tract socioeconomic status (SES) data were missing for 2.4 percent of patients. b p < 0.05 (test for trend). c p < 0.05 (chi-square test of independence). 162 January/February 2008

5 associations between EOL-IEI and the use of recommended care (Exhibit 2). Stage III colon cancer patients were more likely to receive adjuvant chemotherapy in high versus low EOL-IEI areas. However, increasing EOL-IEI was not associated with recommended adjuvant therapy for patients with rectal cancer. In addition, colorectal cancer patients were moderately more likely to be diagnosed with incurable (stage IV) disease in high EOL- IEI areas. The EOL-IEI also had mixed associations with colonoscopy and other testing. Increasing EOL-IEI was not associated with complete diagnostic colonoscopy or surveillance colonoscopy. However, patients were more likely to receive a recommended schedule of CEA surveillance tests following surgery in high EOL-IEI areas. Recommended, discretionary, or nonrecommended chemotherapy. For colon cancer patients of all stages, the adjusted likelihood of receiving chemotherapy increased by 0.9 [0.5, 1.3] percentage points for each $1,000 increase in EOL-IEI (Exhibit 3). Receipt of chemotherapy was related to EOL-IEI for patients at all stages of diagnosis, including those for whom chemotherapy is recommended (stage III), not recommended (stage I), and discretionary (stage II). Overall use of chemotherapy and the absolute increase associated with EOL-IEI was highest for patients most likely to benefit (stage III). However, substantial portions of patients in all areas underwent chemotherapy in discretionary cases. In addition, for each $1,000 increase in end-of-life inpatient spending, discretionary use increased approximately one percentage point. We found increased use of chemotherapy at all levels of comorbidity in high EOL-IEI areas (Exhibit 4), resulting in larger numbers of patients with comorbid illness undergoing treatment in high- versus low-spending areas. Looking at chemotherapy use by age, we found increased use only in older patients: Patients who received treatment were approximately 0.6 years older in the highest versus lowest quintile of end-of-life inpatient spending. 17 Mortality. EOL-IEI was not associated with all-cause or cancer mortality but was associated with increased noncancer mortality (Exhibit 5). Examining patients according to EXHIBIT 2 Adjusted Rates Of Recommended Care According To Area-Level Spending Quintile of area-level spending Measure Change in probability for each $1,000 increase in area spending [95% CI] Diagnosed with late-stage disease Adjuvant chemotherapy for stage III colon cancer 17.3% % % % % % [, 0.4] 1.6 [0.8, 2.5] Adjuvant chemotherapy and radiation for stage II or stage III rectal cancer Surveillance colonoscopy within one year after surgery [ 0.7, 1.2] 0.3 [ 0.9, 1.4] Diagnostic colonoscopy prior to or at the time of surgery Surveillance CEA testing [ 0.2, 0.7] 2.5 [1.3, 3.7] SOURCE: Authors tabulations based on Surveillance, Epidemiology, and End Results (SEER) Medicare data. NOTES: Adjusting for patient age, race, ethnicity, sex, marital status, prior history of cancer, tumor grade (except diagnosed with late-stage disease), tumor size (except diagnosed with late-stage disease), tumor stage (except diagnosed with late-stage disease), year of diagnosis, proportion of high school graduates in the census tract of residence, median household income of the census tract of residence, and comorbid illness; models account for clustering at the level of the Hospital Referral Region. Area-level spending measured using the end-of-life inpatient expenditure index (EOL-IEI). HEALTH AFFAIRS ~ Volume 27, Number 1 163

6 Health Tracking EXHIBIT 3 Adjusted Rates Of Chemotherapy In Colon Cancer Patients According To Stage At Diagnosis And Area-Level Spending Quintile of area-level spending Stage at diagnosis Change in probability for each $1,000 increase in area spending [95% CI] All stages Not recommended Stage % % % % % % [0.5, 1.3] 0.3 [, 0.6] Discretionary Stage II Recommended Stage III Discretionary Stage IV [0.4, 1.4] 1.4 [0.7, 2.2] 1.0 [, 2.1] SOURCE: Authors estimations based on statistical models fit to Surveillance, Epidemiology, and End Results (SEER) Medicare data. NOTES: Adjusting for patient age, race, ethnicity, sex, marital status, prior history of cancer, tumor grade, tumor size, year of diagnosis, proportion of high school graduates in the census tract of residence, median household income of the census tract of residence, and comorbid illness; models account for clustering at the level of the Hospital Referral Region. The model for all stages also adjusted for stage. Area-level spending measured using the end-of-life inpatient expenditure index (EOL-IEI). stage at diagnosis, we saw a trend toward reduced colorectal cancer deaths for patients initially diagnosed with stage III disease (p = 0.09). 18 However, in stage II patients, a $1,000 increase in EOL-IEI was associated with increased all-cause mortality (0.6 [0.2, 1.0] percentage points) and noncancer mortality (0.5 [, 0.8] percentage points). For patients diagnosed with stage IV cancers, death from noncancer causes was 0.4 [0.0, 0.8] percentage points higher for each $1,000 increase in EOL- IEI. Discussion We explored the relationship between area-level end-of-life spending, utilization patterns, and mortality for patients with colorectal cancer. We found mixed results in the association between area-level inpatient endof-life spending and recommended care but consistently higher use of a relatively costly cancer treatment (chemotherapy) and few differences in patient outcomes across areas. These results have important implications for the debate over the appropriate policy re- EXHIBIT 4 Proportion Of Patients Who Received Chemotherapy According To Age, Comorbidity, And Area-Level Spending Quintile of area-level spending Patient age and comorbidity Percent change (quintile 5 versus quintile 1) Age % % % % % % 14.6 a 45.1 a Comorbidity Charlson score = 0 Charlson score = 1 Charlson score = a 13.8 a 16.4 a SOURCE: Authors estimations based on Surveillance, Epidemiology, and End Results (SEER) Medicare data. NOTE: Area-level spending measured using the end-of-life inpatient expenditure index (EOL-IEI). a p < 0.05 (test for trend). 164 January/February 2008

7 EXHIBIT 5 Adjusted Three-Year Mortality In Colorectal Cancer Patients According To Area-Level Spending, By Cause Of Death Percent All causes Colorectal cancer Noncancer 2 3 Quintile of area-level spending 4 5 SOURCE: Authors estimations based on statistical models fit to Surveillance, Epidemiology, and End Results (SEER) Medicare data. NOTES: Adjusting for patient age, race, ethnicity, sex, marital status, prior history of cancer, tumor grade, tumor size, tumor stage, year of diagnosis, proportion of high school graduates in the census tract of residence, median household income of the census tract of residence, and comorbid illness; models account for clustering at the level of the Hospital Referral Region. Arealevel spending measured using the end-of-life inpatient expenditure index (EOL-IEI). EOL-IEI was not associated with all-cause or colorectal cancer mortality but was associated with increased noncancer mortality ( p < 0.001). sponse to spending variations. First, we found no association between end-of-life inpatient spending and three of six recommended measures and a modest increase in diagnosis with incurable disease in high-spending areas. This is consistent with previous work documenting little correlation between quality of care and area-level spending. 19 Wedidfindamodest positive association between such spending and the receipt of chemotherapy for stage III colon cancer, an effective but relatively expensive service, although we did not observe similar increases in use of effective adjuvant therapies for rectal cancer. We also observed an eleven-percentage-point increase in CEA surveillance testing in the highest versus lowest EOL-IEI quintile, which suggests that some of the increased testing in high-spending areas documented previously may be beneficial. Second, building on prior work that examined the clinical characteristics of patients who receive services in high- versus lowspending areas, we observed increased use of cancer treatments among older patients and those with higher levels of comorbid illness in areas with higher levels of end-of-life inpatient spending. 20 This observation suggests that high-spending areas may be treating patients who are less likely to benefit from treatment based on competing morbidity and limited life expectancy. We also found that patients in areas with higher EOL-IEI more often received chemotherapy in cases where it was recommended, not recommended, and of uncertain benefit consistent with previous literature on other diseases. 21 New insights. These results also provide some new insights into why this and previous studies failed to observe an association between area-level spending and patient outcomes. First, the potential beneficial effects of providing therapies when they are effective may be diluted by increased use of therapies that are potentially harmful or of little benefit. Randomized clinical trial data have shown sizable mortality reductions for stage III but not stage II colon cancer patients treated with adjuvant chemotherapy. 22 We observed declines in cancer mortality within three years after diagnosis in stage III patients in our sample, consistent with this literature. However, our area-level analysis may have been underpowered to detect these effects, and our findings were not statistically significant. In contrast, we observed a modest increase in allcause and noncancer mortality among stage II patients, which suggests that providing toxic HEALTH AFFAIRS ~ Volume 27, Number 1 165

8 Health Tracking therapies that have not been proved to be effective may actually be harmful, particularly in elderly populations with high levels of comorbid disease. In addition, we observed increases in noncancer mortality among stage IV patients in high-spending areas. During the time period studied, chemotherapy for stage IV colon cancer prolonged life by several months, but at risk of more side effects and treatment-related complications, necessitating careful selection of patients for treatment based on their likelihood of tolerating chemotherapy. 23 Larger numbers of older patients and patients with high levels of comorbidity received chemotherapy in high EOL-IEI areas, which could contribute to increases in noncancer mortality, possibly because chemotherapy-related deaths might not consistently be attributed to the cancer. 24 Taken together, our results suggest that the potential beneficial effects of providing recommended therapies may be diluted or negated by greater use of therapies that are of little benefit or potentially harmful. Strengths and limitations. The strengths of our study include the use of detailed clinical data allowing us to examine clinical characteristics of patients receiving services, long-term follow-up, and the use of a spending measure that isolates spending variations due to true differences in practice patterns from variations in patient illness severity. Our findings, however, should be interpreted in light of several limitations. First, even though our spending measure captured variation in the treatment of patients with identical life expectancy, we did observe some differences across spending quintiles in observed characteristics, including more comorbid disease. Patients in high-spending areas were more likely to be excluded from our study because of health maintenance organization enrollment and less likely, because of incomplete stage information. 25 Although we controlled for age, comorbidity, and disease characteristics including tumor size in our adjusted analyses, we cannot be sure that our results are not in part attributable to unobserved differences in patient populations across areas. However, because high managed care penetration in high-spending areas likely leads to a sicker pool of fee-for-service enrollees, our findings may in fact be understated. Second, we studied people living in U.S. regions with SEER registries, where people are similar to the general U.S. population in terms of age and sex, but with more who are nonwhite, living in urban areas, and enrolled in Medicare managed care than in the general U.S. population. 26 Nevertheless, this population-based sample includes cancer patients from areas representing 14 percent of the U.S. population, and spending variation across these areas was similar to that observed in national samples. 27 Third, most of our patients in the highest-spending areas were reported by the Los Angeles or Detroit registry, and characteristics of these areas might determine practicepatternsthatwewereunabletocontrol for in our analyses. However, because our models examined linear trends in area-level spending across all registries, our results are not driven solely by these two registries. Policy implications. In this study, we found that high EOL-IEI areas were more likely to provide recommended care in some cases. However, increased use of recommended care was offset by increased use of care that was ineffective or potentially harmful, and patient outcomes were generally similar across areas. These results suggest that policies should be designed to help match services to patients most likely to benefit. Policies that would encourage the use of effective therapies while limiting wasteful spending on ineffective care contrast with responses to area-level variation that focus mainly on cost containment. For example, some have suggested that if high-spending areas were forced to mirror low-spending areas, Medicare could save 29 percent. 28 Similarly, imposing limits on the growth of medical costs was a central element of the Clinton administration s health reform plan and remains a part of proposals for single-payer or quasi-single-payer health plans. 29 Theseproposalsaremotivatedbythe observation that medical care is cheaper in countries with cost limits, without apparent 166 January/February 2008

9 adverse health outcomes. 30 Our analysis suggests that such a focus on cost containment is too blunt. Similar outcomes across spending quintiles mask variation in both effective and ineffective care in this colorectal cancer cohort. An across-the-board reduction in services may eliminate valuable care in addition to reducing wasteful care. Regardless of any cost-saving implications, policies designed to rein in discretionary and nonrecommended care while encouraging the use of recommended care would yield greater value for medical spending. 31 An earlier version of this paper was presented at the AcademyHealth annual meeting in Orlando, Florida, in June This work was funded by Grant no. CA from the National Cancer Institute (NCI). Nancy Keating s effort was also funded in part by a Clinical Scientist Development Award from the Doris Duke Charitable Foundation; Amitabh Chandra s effort was funded through Grant no. P01 AG from the National Institute on Aging. This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the Applied Research Program, NCI; the Office of Research, Development, and Information, CentersforMedicareandMedicaidServices; Information Management Services Inc.; and the SEER Program tumor registries in the creation of the SEER- Medicare database. The authors thank Yang Xu and Rita Volya for expert programming assistance. NOTES 1. J.E. Wennberg and M.M. Cooper, The Dartmouth Atlas of Health Care 1999 (Chicago: American Hospital Publishing, 1999). 2. W.P. Welch et al., Geographic Variation in Expenditures for Physicians Services in the United States, New England Journal of Medicine 328, no. 9 (1993): ; J.S. Skinner, E.S. Fisher and J.E. Wennberg, The Efficiency of Medicare, in Analyses in the Economics of Aging, ed.d.a.wise(chicago: University of Chicago Press, 2005), ; and K. Baicker and A. Chandra, The Productivity of Physician Specialization: Evidence from the Medicare Program, American Economic Review 94, no. 2 (2004): K. Baicker and A. Chandra, Medicare Spending, the Physician Workforce, and Beneficiaries Quality of Care, Health Affairs (2004): w184 w197 (published online 7 April 2004; 1377/ hlthaff.w4.184); E.S. Fisher et al., The Implications of Regional Variations in Medicare Spending, Part 1: The Content, Quality, and Accessibility of Care, Annals of Internal Medicine 138, no. 4 (2003): ; and Part 2: Health Outcomes and Satisfaction with Care, Annals of Internal Medicine 138, no. 4 (2003): J.E. Wennberg, E.S. Fisher, and J.S. Skinner, Geography and the Debate over Medicare Reform, Health Affairs 21 (2002): w96 w114 (published online 13 February 2002; 1377/hlthaff.w2.96). 5. A. Jemal et al., Cancer Statistics, 2007, Cancer Journal for Clinicians 57, no. 1 (2007): 43 66; N.L. Keating et al., Do Increases in the Market Share of Managed Care Influence Quality of Cancer Care in the Fee-for-Service Sector? Journal of the National Cancer Institute 97, no. 4 (2005): ; D. Schrag et al., Age and Adjuvant Chemotherapy Use after Surgery for Stage III Colon Cancer, Journal of the National Cancer Institute 93, no. 11 (2001): ; and D. Schrag et al., Who Gets Adjuvant Treatment for Stage II and III Rectal Cancer? Insight from Surveillance, Epidemiology, and End Results Medicare, Journal of Clinical Oncology 19, no. 17 (2001): A.L. Potosky et al., Potential for Cancer Related Health Services Research Using a Linked Medicare-Tumor Registry Database, Medical Care 31, no. 8 (1993): For more details regarding the inclusion and exclusion criteria for our analytic cohort, see Exhibit A1 in the online appendix at healthaffairs.org/cgi/content/full/27/1/159/dc1. 8. Previous studies have used an end-of-life spending index that included spending for both inpatient care and physician services, whereas we used a publicly available measure that accounts for inpatient spending only. However, hospital care represents the largest and most variable category of spending. C. Smith et al., National Health Spending in 2004: Recent Slowdown Led by Prescription Drug Spending, Health Affairs 25, no. 1 (2006): ; and Fisher et al., The Implications of Regional Variations, Part Fisher et al., The Implications of Regional Variations, Part Ibid.; Fisher et al., The Implications of Regional Variations, Part 2 ; and Skinner et al., The Efficiency of Medicare. 11. See, for example, NIH Consensus Conference: Adjuvant Therapy for Patients with Colon and Rectal Cancer, JournaloftheAmericanMedicalAssociation 264,no.11(1990): ;C.E.Desch et al., Recommended Colorectal Cancer Surveillance Guidelines by the American Society of HEALTH AFFAIRS ~ Volume 27, Number 1 167

10 Health Tracking Clinical Oncology, Journal of Clinical Oncolcology 17, no. 4 (1999): 1312; and M.J. Edelman, F.J. Meyers, and D. Siegel, The Utility of Follow-up Testing after Curative Cancer Therapy: A Critical Review and Economic Analysis, Journal of General Internal Medicine 12, no. 5 (1997): For details on the level of evidence supporting each measure and on the eligibility criteria and coding specifications, see Exhibit A2 in the online appendix, as in Note Cause of death was available through 31 December To maximize inclusion of a majority of our cohort, we analyzed three-year mortality, restricting mortality analyses to patients diagnosed before 31 December C.N. Klabunde et al., Development of a Comorbidity Index Using Physician Claims Data, Journal of Clinical Epidemiology 53, no. 12 (2000): Cut points for the quintiles of EOL-IEI were set so that each quintile would have approximately equal numbers of study participants. 15. In sensitivity analyses, we repeated test of trends using quintile of EOL-IEI instead of actual inpatient spending. Our results were not changed. 16. For more socioeconomic and clinical characteristics according to EOL-IEI quintile, see Exhibit A3 in the online appendix, as in Note We found that older and sicker patients underwent chemotherapy in high EOL-IEI areas in most cases when we examined these trends in stage-specific cohorts. 18. For stage-specific results, see Exhibit A4 in the online appendix, as in Note Baicker and Chandra, Medicare Spending ; Fisher et al., The Implications of Regional Variations, Part 1 and Part 2 ; and Skinner et al., The Efficiency of Medicare. 20. Fisher et al., The Implications of Regional Variations, Part 1 ; and J.E. Wennberg et al., Use of Hospitals, Physician Visits, and Hospice Care during Last Six Months of Life among Cohorts Loyal to Highly Respected Hospitals in the United States, British Medical Journal 328, no (2004): M.R. Chassin et al., Does Inappropriate Use Explain Geographic Variations in the Use of Health Care Services? A Study of Three Procedures, Journal of the American Medical Association 258, no. 18 (1987): ;E.Guadagnolietal., Impact of Underuse, Overuse, and Discretionary Use on Geographic Variation in the Use of Coronary Angiography after Acute Myocardial Infarction, Medical Care 39, no. 5 (2001): ; and L.L. Leape et al., Does Inappropriate Use Explain Small-Area Variation in the Use of Health Care Services? Journal of the American Medical Association 263, no. 5 (1990): NIH Consensus Conference ; D.J. Sargent et al., A Pooled Analysis of Adjuvant Chemotherapy for Resected Colon Cancer in Elderly Patients, New England Journal of Medicine 345, no. 15 (2001): ; and J.A. Meyerhardt and R.J. Mayer, Systemic Therapy for Colorectal Cancer, New England Journal of Medicine 352, no. 5 (2005): Meyerhardt and Mayer, Systemic Therapy for Colorectal Cancer ; and NCCN Colon Cancer Clinical Practice Guidelines in Oncology, Journal of the National Comprehensive Cancer Network 1, no. 1 (2003): The validity of cause-of-death information from state death certificates has been questioned. See P. Bach et al., Patient Demographic and Socioeconomic Characteristics in the SEER-Medicare Database: Applications and Limitations, Medical Care 40,no.8Supp.(2002):IV-19 IV For details on number of excluded patients across spending quintiles, see Exhibit A1 in the online appendix, as in Note J.L. Warren et al., Overview of the SEER-Medicare Data: Content, Research Applications, and Generalizability to the United States Elderly Population, Medical Care 40, no. 8 Supp. (2002): IV-3 IV-18; and A.B. Nattinger, T.L. McAuliffe, and M.M. Schapira, Generalizability of the Surveillance, Epidemiology, and End Results Registry Population: Factors Relevant to Epidemiologic and Health Care Research, Journal of Clinical Epidemiology 50, no. 8 (1997): For example, the range between the 5th and 95th percentiles in EOL-IEI is $4,600 across all 307 HRRs compared to $4,900 across the 43 HRRs within the SEER regions. 28. Wennberg et al., Geography and the Debate. 29. J.S. Hacker, Health Care for America: A Proposal for Guaranteed, Affordable Health Care for All Americans Building on Medicare and Employment-Based Insurance, Briefing Paper no. 180 (Washington: Economic Policy Institute, 2007); and S. Woolhandler et al., Proposal of the Physicians Working Group for Single-Payer National Health Insurance, Journal of the American Medical Association 290, no. 6 (2003): U.E. Reinhardt, P.S. Hussey, and G.F. Anderson, U.S. Health Care Spending in an International Context, Health Affairs 23, no. 3 (2004): 10 25; and C. Schoen et al., U.S. Health System Performance: A National Scorecard, Health Affairs 25, no. 6 (2006): w457 w475 (published online 20 September 2006; 1377/hlthaff.25.w457). 31. D.M. Cutler, YourMoneyorYourLife:StrongMedicine for America s Health Care System(Oxford: Oxford University Press, 2004). 168 January/February 2008