Colon Cancer Treatment Costs For Medicare And Dually



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Colon Cancer Treatment Costs \for Medicare and Dually Eligible Bene\ficiaries Zhehui Luo, Ph.D., Cathy J. Bradley, Ph.D, Bassam A. Dahman, and Joseph C. Gardiner, Ph.D. To estimate the cost attributable to colon cancer treatment 1 year after \fiag -nosis by cancer stage, comorbi\fity, treat -ment regimen, an\f \be\ficai\f eligibility, we extracte\f an inception cohor t of colon cancer patients age\f 66 an\f ol\fer \fiagnose\f between 1997 an\f 2000 from the \bichigan Tumor Registr y. Patients were matche\f to non-cancer control subjects in the \be\ficare Denominator �le. We use\f the \fif ference-in -\fif ferences metho\f to estimate costs attrib -utable to cancer, controlling for costs prior to \fiagnosis. The mean total colon cancer cost per \be\ficare patient was $29,196. The metho\f can be applie\f to longitu\final \fata to estimate long term costs of cancer from inception where inci\fent patients are i\fenti�e\f from a tumor registr y. introduCtion The cost o\f colorectal cancer has recently been the sub\bect o\f several sci -enti�c investigations (Wright et al., 2007; Yabro\f \f et al., 2007a; War ren et al., 2008). These investigations were most likely spur red by recent screening initiatives and e\f \for ts to raise public awareness o\f colorectal cancer. Accurately estimating the direct medical cost o\f cancer is relevant to policymakers weighing new options \for cancer prevention and control, screening Zhehui Luo and Joseph C. Gardiner are with Michigan State University. Cathy J. Bradley and Bassam A. Dahman are with Virginia Commonwealth University. The research in this ar ti-cle was suppor ted by National Cancer Institute Grant Number R01-CA101835-01. The statements expressed in this ar ticle are those o\f the authors and do not necessarily re\flect the views or policies o\f Michigan State University, Virginia Commonwealth University, National Cancer Institute, or Centers \for Medicare & Medicaid Ser vices (CMS). guidelines, and treatments. A descrip -tive review o\f cancer cost studies \found signi�cant heterogeneity in estimation methods, study settings, populations, and measurements o\f cost (Yabro\f \f et al., 2007b). Past analyses o\f the cost o\f cancer treatment \focused on long-term aggre -gate estimates (Brown et al., 1999; 2002; Etzioni et al., 2002) and were not designed to answer questions related to patient characteristics or treatment regimens. In this study, we have two ob\bectives: (1) to extend prior studies by estimating the cost attributable to colon cancer 1 year a\fter diagnosis by cancer stage, comorbidity, treatment regimen, and other patient char -acteristics; and (2) to estimate the di\f \fer -ences in 1-year cost between Medicare only and the dually eligible bene�ciaries. Colon cancer usually occurs later in li\fe (at age 60 to 70 years), and Medicare and Medicaid are the primar y payers o\f cancer care. We \focused on colon cancer instead o\f colorectal cancer because the cost o\f rectum cancer is usually higher and because colon cancer is among the cancer sites where screening, early detection, and e\f \fective treatment are \feasible and proven to reduce mor tality (Midgley and Ker r, 2005). Individuals who receive health care coverage \from the Medicare and Medicaid Programs \for at least 12 months prior to the diagnosis o\f cancer are de�ned as dual eligibles in this and our previous study (Bradley, Luo, and Given, 2008). Dually eligible bene�ciaries are more likely to live under the Federal pover ty level, reside in nursing homes or live alone, be \from a minority population and unmar ried, and HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 35

have lower education attainment (Mur ray and Shatto, 1998). Studies have \found that Medicaid patients are less likely to receive cancer screening and more likely to be diagnosed at a later cancer stage than are Medicare only patients (Ward et al., 2008). An inquir y on cancer cost di\f \feren -tials by cancer stage, treatment procedure and comorbidity between Medicare only and dually eligible groups can shed light on disparity in healthcare utilization. Our method o\f estimating 1-year cost takes into account prior year non-cancer costs and treatment received. data and \betHods Cancer Patients We used statewide Medicaid and Medicare data merged with the Mich -igan T umor Registr y to extract a study sample o\f patients with a �rst primar y colon cancer diagnosis in the years 1997 through 1999. The Michigan Cancer Sur -veillance Program, which maintains the Michigan T umor Registr y, is more than 95% complete based on exter nal audit �ndings. For details o\f the linkage process, see Bradley et al. (2007). This study was approved by Institutional Review Boards at the Michigan Depar t -ment o\f Community Health, Michigan State University, and Virginia Common -wealth University. From statewide Medicare �les, we extracted all claims \for inpatient, outpa -tient, physician ser vices, and hospice during the study period \for all patients who cor rectly matched to the Michigan State segment o\f the Medicare Denomi -nator �le (approximately 89% o\f patients) and were enrolled in Par ts A and B. Patients enrolled in Par t A only were excluded \for lack o\f physician o\f �ce visit in\formation. We identi�ed 8,157 Medicare Par ts A and B bene�ciaries aged 66 years and older who had a �rst primar y colon cancer diagnosis \from 1997 to 1999. Our data -base contains claims \from Januar y 1996 to December 2000 so that all patients had at least 12 months o\f data be\fore and a\fter the month o\f diagnosis. We excluded patients enrolled in managed care ( n=512) because their claims were not available. We also excluded cancer patients who had no claims ( n=144) or had zero cost ( n =22) during the study period. Patients with invasive but unknown stage o\f can -cer were excluded because we could not assign these patients to a speci�c stage ( n =782). Patients o\f other or unknown race ( n =128) were excluded to avoid mismatch with controls (see non-cancer sub\bect section below). Finally, 107 patients were excluded because they did not have a matched control sub\bect or their matched controls had no claims or valid cost data in the study period. The remaining sample size was 6,462 o\f which 765 were continu -ously insured by Medicaid since the time o\f diagnosis in addition to Medicare. Claims data were used to identi\fy treatment. Surger y procedures were identi�ed in the inpatient and outpatient �les using Inter national Classi�cation o\f Disease, 9th Edition (ICD-9) codes.1 Chemotherapy initiation was identi�ed by at least one claim indicating the adminis -tration o\f chemotherapy within 6 months \following diagnosis.2 Hershman et al. (2006) \found that 91% o\f elderly colon cancer patients initiate chemotherapy within 3 months o\f diagnosis. 1 The ICD-9 codes were 45.71-45.79, 45.8, 48.41-48.49, 48.50, and 48.61-48.69.�2 Chemotherapy was identi\fied by the Current Procedural Ter-minology (CPT) codes 96400–96599; Health Care Common Pro-cedural Codes Q0083–Q0085, J8510, J8520, J8521, J8530–J8999, J9000–J9999, J0640\t; and ICD-9 codes E\t0781, E9331, and V5\t8.1.�HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 36

non-Cancer subjects To attribute costs to a par ticular disease, researchers have examined and designated each claim as related to or not related to the disease under study (Finkelstein et al., 2003). However, dis -ease causality and concur rence is a complex phenomenon. For example, depression has been \found to be both a risk \factor \for cancer (Gallo et al., 2000) and a consequence (Polsky et al., 2005) o\f cancer, but including depression treat -ment as a “cancer cost” is questionable. There\fore, researchers have tur ned to matching cancer patients to non-cancer controls and comparing costs in each group to distinguish between cancer and non-cancer treatments. Various matching methods have been applied to match patients with and without the disease under study. We took a broader perspec -tive to assess cancer costs by randomly selecting up to three control sub\bects to each cancer patient matched on age, race, sex, and health ser vice area o\f residence. We used the cancer patient’s date o\f diagnosis as the re\ference date \for the matched controls to establish a pre- and post diagnosis period. outcome and Control variables The primar y outcome o\f interest was the total cost o\f cancer treatment in the year a\fter diagnosis or until death within 1 year o\f diagnosis. Previous research has shown that most shor t-term cancer cost occurs within the �rst year o\f diag -nosis (Delco et al., 2005). Medicare covers inpatient ser vices (Par t A) and outpatient ser vices (Par t B). We used the sum o\f Medicare payment, patient deductible and coinsurance amount, and the third-par ty payer paid amount as a proxy \for the value o\f medical ser vices. All cost estimates are in 2000 dollars de�ated by the Medicare Economic Index.3 Closely associated with cost and treat-ment options is sur vival. Patients’ sur vival was ascer tained through the Medicare Denominator �le and National Death In -dex. Dually eligible breast cancer patients had poorer 8-year sur vival compared with Medicare only patients (Bradley et al., 2005). Patients who die within 1 year o\f diagnosis may have higher or lower costs depending on the length o\f sur vival and treatments received. Brown et al. (1999) \found that the content o\f care \for patients with shor t sur vival is more similar to that o\f the last year o\f li\fe phase than that o\f the initial phase. Because the cost in the last year o\f li\fe phase is much higher than the cost in the initial phase, we may expect higher cost in the year a\fter diag -nosis among those with shor t sur vival than those who sur vive more than 1 year. We de�ned cancer stage using the Sur veillance Epidemiology and End Results (SEER) summar y stages (in situ, local, regional, and distant) and excluded patients with unknown stage. We con -str ucted the Deyo, Cherkin, and Ciol (1992) and Klablunde et al. (2000) adap -tation o\f the Charlson Comorbidity Index as comorbidity burden \for cancer patients and their controls be\fore and a\fter the diagnosis or re\ference date. We used patients’ inpatient, outpatient, and physi -cian claims to constr uct the Comorbidity Index, which was grouped into categories 0, 1, 2, and ≥3. Data on patient age, race, and sex were obtained \from the Michigan Tumor Reg -istr y. Age was grouped into the \following categories: 66 to 70 years, 71 to 75 years, 76 to 80 years, and older than 80 years. Based on patients’ address, we linked the census tract median household income 3 We did not use the Hospital Wage Index to ad\bust \for in\flation in Par t A costs because o\tur data are \from a single State. HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 37

and education level to each patient. The income categories were <$25,000; $25,001 to $35,000; $35,001 to $45,000; and >$45,000. Education in each census tract was measured by the percentage o\f the population with less than high school, high school but not college, and college or more education. Missing values in income and education were imputed using the mean imputation method.4 Based on patients’ county o\f residence, we obtained the number o\f shor t-term hospitals with oncology ser vices and the number o\f colon/rectum surgical specialists as mea -sures o\f county-level resource availability. Ad\buvant and palliative chemotherapy is the standard treatment \for advanced stage cancer, and recent evidence sug -gests the use o\f chemotherapy in stage II cancer as well. Thus, we categorize cancer treatments to three groups: no resection (including those with no ad\buvant treat -ment and those with chemotherapy only ( n = 1,177 [18.21%]), one or more resection without chemotherapy ( n=3,665 [56.72%]), and one or more surgeries with chemo -therapy ( n=1,620 [25.07%]).5 statistical \bethods Our �rst ob\bective was to estimate the mean cost attributable to cancer 1 year a\fter diagnosis. Three \features o\f cost data presented themselves immediately. First, a substantial propor tion o\f patients had zero cost in the 12 months be\fore diagnosis and a substantial propor tion o\f control patients had zero cost in both periods. Second, costs \for cancer patients in the 12 months a\fter diagnosis had a di\f \ferent distribution than costs \for cancer 4 The number o\f patients with imputed income and education value is 284 (4.4%) and 315 (4.9%) in the \final sample. Excluding these patients did no\tt change the results substantively. 5 The number o\f patients with more than one resection is 147, among which 101 did not have chemotherapy, and 34 did. The sample is too small\t to provide separate esti\tmates. patients in the 12 months be\fore diagnosis and \for control patients in both periods. Finally, the expenditure data were highly skewed. Because o\f these \features, we used strategies other than Ordinar y Least Squares regression to estimate the mar -ginal e\f \fect o\f patient characteristics on mean cancer costs. First, we \formulated a two-par t model (Mullahy, 1998) \for costs o\f control patients in both periods and o\f cancer patients in the 12 months be\fore diag -nosis, which contain many obser vations with zero cost. The �rst par t o\f the two-par t model estimates the probability o\f any cost, speci�ed as a probit (Equation 1) or logit (Equation 2) model. Pr( yit>0|xit) = Φ(x ′ it) (1) Pr( yit>0|xit) = exp( x′ it)

in retrans\formation to the original scale o\f costs. In addition, i\f the variance o\f the er rors is related to covariates, then retrans\formed mean estimates could be biased (Manning, 1998; Duan, 1983). The last approach su\f \fers \from the dimen -sionality problem as well as di\f �culties in interpretation. We \followed Manning and colleagues (2005) and systematically com -pared log-, square-root, Box-Cox trans\for -mation, and GLM with gamma distribution through a series o\f tests \for distribution, nonlinearity, speci�cation, goodness o\f �t, and over �tting. For the non-zero par t o\f the two-par t model, the Park test was used to gauge the selection o\f the distri -butions. The Pregibon Link test and the RESET test were used \for nonlinearity o\f the speci�cation, the modi�ed Hosmer-Lemeshow test was used \for goodness o\f �t, and the Copas test was used \for over-�tting using split sample cross-valida -tion.6 The best �tted models were used to estimate mean total medical cost in each period \for cancer and control patients who had incur red any cost. Combining the �rst and second par t o\f the two-par t model together and the stand-alone par t \for the cost o\f cancer patients a\fter diagnosis (always positive), we estimated the expected values \for all medical costs \for cancer and control patients be\fore and a\fter the diagnosis or re\ference date (Equation 4 or 5): = E( yit|yit>0, xit,zit)Pr( yit>0|xit,zit) = \f( z′ it) Φ (x ′ it) (4) E(yit|xit, zit) = E( yit|yit>0, xit,zit)Pr( yit>0|xit,zit) = \f( z′ it) exp( x′ it)�E(yit|xit, zit)

cancer patients with di\f \ferent diagnosis predictions (Basu and Rathouz, 2005). stage, treatment regimen, and comor- This method entails comparisons o\f two bidity, we used the method o\f recycled predictive margins where a par ticular Table 1�Characteristic of \dColon Cancer Patie\dnts and Control \fub\djects�Cancer Cases (N=6,4\(62\b Control Subjects (N\(=11,483\b N (%\b N (%\b Age 66-70 years 1251 19.36 2203 19.18 71-75 years 1629 25.21 2920 25.43 76-80 years 1542 23.86 2777 24.18 >80 years 2040 31.57 3583 31.2 Race White 5711 88.38 10123 88.16 African American 751 11.62 1360 11.84 Sex Male 2841 43.96 5039 43.88 Female 3621 56.04 6444 56.12 SEER Stage In situ 275 4.26 n.a. n.a. Local 2412 37.33 n.a. n.a. Regional 2631 40.71 n.a. n.a. Distant 1144 17.7 n.a. n.a. Census tract ≤$25k 1853 28.68 3305 28.78 median annual income $25k to ≤$35k 2053 31.77 3644 31.73 $35k to ≤$45k 1449 22.42 2566 22.35 >$45k 823 12.74 1454 12.66 Missing 284 4.39 514 4.48 N (%\b N (%\b Charlson Index 0* 4264 65.99 8048 70.09 1* 1271 19.67 2080 18.11 2 500 7.74 805 7.01 3+* 427 6.61 550 4.79 Myocardial infarct\(ion 105 1.62 154 1.34 Congestive heart fa\(ilure* 646 10 895 7.79 Peripheral vascular\( disease 203 3.14 347 3.02 Cerebrovascular dis\(ease 340 5.26 587 5.11 Obstructive pulmona\(ry disease* 701 10.85 1011 8.8 Dementia* 74 1.15 229 1.99 Diabetes* 1008 15.6 1373 11.96 Chronic renal fail\(ure 74 1.15 144 1.25 Ulcer* 129 2 127 1.11 Rheumatism 96 1.49 172 1.5 Alzheimer’s diseas\(e* 123 1.9 363 3.16

attribute (such as dual eligibility) is assumed present or absent (Graubard and Kor n, 1999). Because o\f the complexity o\f the model, we obtained bootstrap standard er rors and bias-cor rected boot -strap con�dence inter vals o\f the predicted di\f \ferences in total cost between Medicare only and dually eligible patients. results Table 1 repor ts the demographic and comorbid conditions o\f the cancer patients and the controls. Age, race, sex, and health ser vice areas were distributed evenly due to matching. Cancer patients had higher comorbidity in the 12 months prior to their diagnosis o\f cancer. More cancer patients had congestive hear t \failure, obstr uctive pulmonar y disease, diabetes, and ulcer, but \fewer cancer patients had dementia and Alzheimer’s disease. There was no statistically signi� -cant di\f \ference in the prevalence o\f other diseases between cancer patients and their matched control sub\bects. Table 2 compares the demographic, comorbid conditions, sur vival, and treat -ment regimens among cancer patients by dual eligibility status. Compared with Medicare only patients, dually eligible patients were older, had higher propor -tions o\f A\frican American and \female individuals, lived in neighborhoods with lower income and education, and had similar cancer stage ( p=0.121) but worse sur vival (Table 3, p<0.001). The dually eligible patients also had higher preva -lence in 12 out o\f the 14 comorbid condi -tions in the Charlson Comorbidity Index. As seen in Table 3, \fewer dually eligible patients received combined resection and chemotherapy treatment as compared to the Medicare only patients (15% versus 26%). Among those who were diagnosed at the in situ/local or regional stage, dually eligible patients were also more likely to receive no treatment or chemo-therapy only and less likely to have com -bined resection and chemotherapy. Dually eligible patients had a higher \fatality rate than the Medicare only group (23% versus 12% \for in situ/local stage; 30% versus 21% \for regional stage).Patients with distant stage o\f cancer had similar sur vival ( p=0.584) and similar treatments ( p =0.133) between the Medicare only and the dually eligible patients. Our sensitivity analysis excluding 1, 2, or 3 months o\f claims be\fore the diagnosis/re\ference date indicated that excluding costs in the month prior to the diagnosis/re\ference date led to com -parable total, inpatient, and outpatient costs between cancer patients and their matched controls in the period be\fore the diagnosis/re\ference date. Thus, our study estimated cost attributable to cancer in a period o\f 13 months: 1 month be\fore the actual diagnosis date and 12 months a\fter diagnosis.7 Unad\busted direct medical costs in the 11-month base period and the 13-month post period \for cancer patients and cor responding costs \for controls sub\bects are summarized in Table 4. Compared to controls, cancer patients had similar total, inpatient, and outpatient costs in the baseline period. However, cancer patients had lower phy -sician and hospice costs. In the post cancer period, the average total costs \for cancer patients were $28,832 higher than the total costs o\f control sub\bects, and the ma\bority o\f this di\f \ference was due to inpatient costs ($20,470). In addi -tion, outpatient and physician o\f �ce costs were higher in cancer patients ($2,361 and $5,522, respectively). In the base period, more cancer patients had inpa -7 Sensitivity analysis results excluding claims 0, 2, or 3 months be\fore diagnosis are available upon request. These analyses did not change the \final prediction o\f total cost attributable to cancer substantially. HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 41

Table 2�Characteristics of Colon Cancer Patients b\b Medicare/Medicaid Eligibilit\b \ftatus�Medicare Only (N=5,\(697\b Dually Eligible (N=\(765\b N (%\b N (%\b Age* 66–70 years 1125 19.75 126 16.47 71–75 years 1438 25.24 191 24.97 76–80 years 1384 24.29 158 20.65 >80 years 1750 30.72 290 37.91 Race* White 5187 91.05 524 68.5 African American 510 8.95 241 31.5 Sex* Male 2613 45.87 228 29.8 Female 3084 54.13 537 70.2 SEER Stage In situ 250 4.39 25 3.27 Local 2140 37.56 272 35.56 Regional 2292 40.23 339 44.31 Distant 1015 17.82 129 16.86 Census tract ≤$25k 1477 25.93 376 49.15 median annual income* $25k to ≤$35k 1797 31.54 256 33.46 $35k to ≤$45k 1380 24.22 69 9.02 >$45k 796 13.97 27 3.53 Missing 247 4.34 37 4.84 Mean Std Mean Std Census tract Percent \f12 year* 0.23 0.1 0.3 0.13 education Percent \fcollege* 0.6 0.09 0.57 0.09 Percent ≥college* 0.17 0.14 0.13 0.1 N (%\b N (%\b Charlson Index 0* 3848 67.54 416 54.38 1* 1097 19.26 174 22.75 2 419 7.35 81 10.59 3+* 333 5.85 94 12.29 Myocardial infarct\(ion 91 1.6 14 1.83 Congestive heart fa\(ilure* 526 9.23 120 15.69 Peripheral vascular\( disease 168 2.95 35 4.58 Cerebrovascular dis\(ease 275 4.83 65 8.5 Obstructive pulmona\(ry disease* 587 10.3 114 14.9 Diabetes* 841 14.76 167 21.83 Chronic renal fail\(ure* 57 1 17 2.22 Ulcer* 105 1.84 24 3.14 Rheumatism 83 1.46 13 1.7 Alzheimer’s diseas\(e* 75 1.32 48 6.27

tient claims as compared to the controls, but \fewer cancer patients had outpatient claims or physician o\f �ce visits. The shape o\f the cost distributions between cancer and control sub\bects be\fore and a\fter the re\ference date were ver y di\f \ferent, indicating the need \for separate estimations o\f the mean cost. Figure 1 displays the box plots o\f the total costs, square-root trans\formation o\f the total costs, and logarithm trans\forma -tion o\f the total costs plus 0.05 \for cancer and control patients in both periods. The shape \for cancer and control sub\bects’ costs in the 11 months be\fore the diag -nosis and re\ference date and the cost \for control sub\bects in the 13 months a\fter the re\ference date are similar. However, the distribution \for cancer patients’ costs in the 13 months a\fter the diagnosis date was ver y di\f \ferent. The Park test, the Pregibon Link test, the RESET test, the modi�ed Hosmer-Lemeshow test, and the Copas test \for the second par t o\f the two-par t model all \favored the gamma distribution in a GLM over the other speci�cations. The modi�ed Hosmer-Lemeshow test and the Copas test \for the overall two-par t model also \favored the GLM gamma distribution over the log-normal distribution. Table 3�One-Year \furvival and Treatment Procedures b\b Medicare/Medicaid Eligibilit\b \ftatus�Medicare Only Dually Eligible N=5,697 N=765 Overall N % N % p-value Death 1488 26.12 262 34.25 \f0.001 None or chemo only\( 1011 17.75 166 21.7 Resection no chemo\( 3184 55.89 481 62.88 Resection with che\(mo 1502 26.36 118 15.42 \f0.001 In Situ/Local Stag\(e N=2,390 N=297 Death 292 12.22 67 22.56 \f0.001 None or chemo only\( 483 20.21 82 27.61 Resection no chemo\( 1667 69.75 203 68.35 Resection with che\(mo 240 10.04 12 4.04 \f0.001 Regional Stage N=2,292 N=339 Death 480 20.94 101 29.79 \f0.001 None or chemo only\( 171 7.46 33 9.73 Resection no chemo\( 1166 50.87 228 67.26 Resection with che\(mo 955 41.67 78 23.01 \f0.001 Distant Stage N=1,015 N=129 Death 716 70.54 94 72.87 0.584 None or chemo only\( 357 35.17 51 39.53 Resection no chemo\( 351 34.58 50 38.76 Resection with che\(mo 307 30.25 28 21.71 0.133 NOTE: The Pearson \(chi-square test wa\(s used for testing\( between Medicare \(only and dually eligible pa\(tients. SOURCE: Michigan Tumor Res\(igtry, Medicare an\(d Medicaid fee-for\(-service claims from 1996 to 200\(0. HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 43

Table 5 repor ts the average o\f predicted costs by cancer stage, age group, comor -bidity, sur vival, and treatment received \for all patients and \for Medicare only and dually eligible patients separately esti -mated through a two-par t model. The average direct medical costs attributable to cancer in 1 year a\fter diagnosis were $29,196. Treatment costs were not statis -tically signi�cantly di\f \ferent between the Medicare only patients and the dually eli -gible patients (∆=$1,272, 95% CI = [–$357, $2,769]). Patients with regional stage cancer at diagnosis had the highest cost ($30,748) \followed by patients diagnosed with distant stage cancer ($29,933) and patients with in situ or local stage cancer ($27,551). The total costs \for the dually eligible patients with regional and distant stage o\f cancer were lower than their Medi-care only counterpar ts by $2,050 ( p<0.1) and by $3,335 ( p<0.1), respectively; and costs \for in situ/local stage cancer were similar between the two groups. Average total cancer costs were $14,696, $28,703, and $42,523 \for patients under-Table 4�Average Costs and Percentage of Patients with Positive Costs for Cancer Patients and Control \fubjects Before and After Diagnosis or Reference Date�Control Cancer Cases Subjects Difference 95% Con�dence Inter\(val 11 months before Total 4757 4653 103 (–221, 428\b reference date – 30 daysa Inpatient 2691 2451 240 (–16, 497\b Outpatient 758 789 –31 (–96, 35\b Physician* 1303 1387 –84 (–147, –21\b Hospice* 3 27 –23 (–35, –12\b 13 months after Total* 34077 5249 28832 (28209, 29455\b reference date – 30a Inpatient* 23234 2767 20470 (19974, 20966\b Outpatient* 3169 808 2361 (2232, 2490\b Physician* 7143 1622 5522 (5361, 5683\b Hospice* 530 52 479 (416, 541\b Cancer Cases Control Subjects N % N % 11 months before Total 5722 88.55 10219 88.99 reference date – 30 daysb Inpatient* 1279 19.79 2038 17.75 Outpatient* 4152 64.25 7815 68.06 Physician* 5240 81.09 9908 86.28 Hospice* 3 0.05 50 0.44 13 months after Total 6462 100 10007 87.15 reference date – 30b Inpatient* b 6043 93.52 2214 19.28 Outpatient* b 5923 91.66 7765 67.62 Physician* b 6150 95.17 9778 85.15 Hospice* b 770 11.92 105 0.91

Figure 1 Box plots for total costs, square-root transformation of the total costs, and the logarithm transformation of total costs plus 0.05 for cancer and control patients in both periods. going no treatment or chemotherapy only, resection, and resection combined with chemotherapy, respectively. The treat -ment costs \for resection combined with chemotherapy were much higher than the other treatment regimens. None o\f the di\f \ferences between the Medicare only and the dually eligible patients by treatment procedures were statistically signi�cant. However, the overall di\f \fer -ence in costs \for patients with combined resection and chemotherapy was substan -tial between the Medicare only and dually eligible patients. Given the same cancer stage, the di\f \ferences were statistically signi�cant \for patients with regional or distant cancer and undergoing both resection and chemotherapy (∆=$3,555, 95% CI = [$182, $6,677] \for regional cancer; ∆=$5,740, 95% CI = [$223, $11,000] \for distant cancer). With more evidence on the bene�ts o\f ad\buvant chemo- therapy in the elderly, the gap between Medicare only and dually eligible patients in treatments needs to be addressed. Patients with more comorbid condi -tions had higher costs than patients with \few comorbid conditions. Medicare only cancer patients without comorbid condi -tions or with only one comorbid condition had higher costs than their dually eligible counterpar ts (∆=$1,109, p<0.1; ∆=$1,280, p <0.1). The di\f \ferences in average total costs \for the other comorbid groups were statistically similar between the Medicare only and the dually eligible patients. Figure 1�Box Plots for Total Costs, \fquare-Root Transformation of Total Costs, and Logarithm Transformation of Total Costs Plus 0.05 for Cancer and Control Patients in Both Periods�Log(T ot al C ost + 0.05) T otal C os t -5 0 5 10 15 0 250K 500K Square R oot of T ot al C ost 0 200 400 600 800 Bef ore Af ter Bef ore Af ter Bef ore Af ter Bef ore Af ter Co n tro l Ca n c er Co n tro l Ca n c er Bef ore Af ter Bef ore Af ter Co n tro l Ca n c er SOURCE: Michigan \(Tumor Registry, Me\(dicare and Medicai\(d fee-for-service c\(laims from 1996 to 200\(0. HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 45

46 Table 5�Predications of Mea\dn Cost Attributabl\de to Cancera�All ($\b Medicare Only ($\b Dually Eligible ($\b Difference ($\b [95%C\(I]b All patients 29196 29342 28070 1272c [–357, 2569] \ftage of Cancer In situ/local 27551 27458 28063 –605 [–3866, 2126] \(Regional 30748 30993 28943 2050c [–341, 4109] Distant 29933 30326 26992 3335c [–64, 6438] Procedure None or Chemo 14696 14760 14276 483 [–2246, 3376] \(Resection alone 28703 28822 27922 900 [–1074, 2468] \(Resection + Chemo \( 42523 42860 40322 2538 [–970, 6073] Comorbidit\b 0 25205 25329 24220 1109c [–143, 2167] 1 27967 28110 26830 1280c [–288, 2532] 2 28300 28465 26985 1480 [–573, 3044] 3+ 32922 33125 31294 1832 [–1297, 4273]\( \ftage of Cancer × P\drocedure In situ/local × No\(ne or Chemo 10920 10844 11403 –560 [–3309, 1909] \(In situ/local × Re\(section alone 27225 27105 27983 –878 [–4390, 1806]\( In situ/local × Re\(section + Chemo 41426 41431 41403 28 [–5867, 4994] Regional × None or\( Chemo 18101 18233 17238 995 [–2839, 4583] Regional × Resecti\(on alone 30308 30543 28775 1768 [–1155, 4141] \(Regional × Resecti\(on + Chemo 41624 42095 38540 3555d [182, 6677] Distant × None or \(Chemo 15332 15546 13908 1638 [–1519, 4578] \(Distant × Resectio\(n alone 27925 28300 25423 2877c [–644, 6166] Distant × Resectio\(n + Chemo 45886 46634 40895 5740d [223, 11000] \ftage of Cancer × C\domorbidit\b In situ/local × 0 \( 21583 21523 21960 –437 [–2734, 1542] \(In situ/local × 1 \( 23207 23141 23615 –474 [–2999, 1732]\( In situ/local × 2 \( 23272 23204 23685 –481 [–3002, 1730]\( In situ/local × 3+\( 24239 24159 24683 –524 [–3295, 1779] \(Regional × 0 27602 27789 26104 1685c [–296, 3439] Regional × 1 25916 29971 27939 2032c [–303, 4165] Regional × 2 28210 28467 26162 2305c [–409, 4813] Regional × 3+ 33611 33969 30756 3213c [–677, 6666] Distant × 0 28090 28408 25557 2851d [12, 5546] Distant × 1 28522 28885 25635 3250c [–76, 6322] Distant × 2 30367 30816 26787 4030c [–198, 7838] Distant × 3+ 25594 26103 21540 4563c [–452, 9088] aRecycled predictio\(n approach was use\(d except for the p\(rediction for all \(patients (�rst row, �rst colu\(mn\b.�bBootstrap bias-cor\(rected con�dence i\(ntervals are used f\(or testing equalit\(y in costs between Medicare \(only and dually el\(igible patients. 1,\(000 cluster bootstrapped sampl\(es were used where\( each cancer patie\(nt and the control\(s were considered one cl\(uster in bootstrap\(ping.�cThe tests between \(Medicare only and \(the dually eligibl\(e patients were si\(gni�cant at p \f0.1 using normal-\(based bootstrap co\(n�dence intervals.�dThe tests between \(Medicare only and \(the dually eligibl\(e patients were si\(gni�cant at p \f0.05 using normal-\(based bootstrap co\(n�dence intervals.�SOURCE: Michigan T\(umor Resigtry, Med\(icare and Medicaid\( fee-for-service cl\(aims from 1996 to 2000.�HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t

Given the same stage o\f diagnosis, patients with more comorbid conditions had higher costs, with the exception o\f patients with distant stage cancer and three or more comorbid conditions who had lower costs than patients with distant stage cancer and \fewer comorbid conditions. Consistent with the main e\f \fects o\f cancer stage on the di\f \ferences o\f average total costs between Medicare only and dually eligible patients, only regional and distant stage cancer patients had marginally sig -ni�cant higher costs among the Medicare only patients. The gap widened by comor -bidity \for each cancer stage. For example, \for patients with regional cancer stage, the di\f \ferences in costs ranged \from $1,685 to $3,213 when comorbidity increased \from 0 to 3 or more. For patients with distant stage cancer and no comorbidity, the di\f \ference in average total costs was sta -tistically signi�cant (∆=$2,851, 95% CI = [$12, $5,546]). sensitivity to Cost outliers T wo cancer patients and one control patient had total costs greater than $250,000 in the period a\fter diagnosis/ re\ference date, and one cancer patient had total costs greater than $500,000 in 1 year a\fter diagnosis (Figure 1). These obser vations increase the skewness o\f the data. Because our goal is not to predict who had outlying costs but to estimate the mean costs \for di\f \ferent patients, we re-estimated the model excluding those obser vations. Without the obser vation greater than $500,000, the mean total cost \for colon cancer was $29,124; and the di\f -\ference between the Medicare only and dually eligible patients was $1,161 (95% CI = [–$180, $2,783]). Without the three obser vations greater than $250,000, the mean total cost \for colon cancer was $29,100; and the di\f \ference between the Medicare only and dually eligible patients was $1,212 (95% CI = [–$239, $2,591]). Unsurprisingly, dropping the large cost obser vations lowered the standard er rors o\f the estimates slightly. No estimates had changed substantially \for any subgroups by stage, treatment, or comorbidity. The statistical signi�cance also remained largely unchanged. discussion Our �ndings provide population-based estimates o\f 1-year costs attributable to colon cancer by stage o\f diagnosis, comorbidity, treatments, and dual eli -gibility status o\f Medicare bene�cia -ries. The mean total cost attributable to colon cancer 1 year a\fter diagnosis was $29,196. Patients diagnosed with in situ and local stage had the lowest costs ($27,551), \followed by patients with distant stage ($29,933), and patients with regional cancer had the highest cost ($30,748). Given the same stage o\f diagnosis, patients with more comorbid conditions had higher costs. Having one, two, or three and more comorbid condi -tions increased costs by $2,762, $3,095, and $7,717, respectively, as compared to patients with no comorbidity. Overall treatment costs were higher among Medicare only patients than among dually eligible patients, but the di\f \ference was not statistically signi�cant. Dually eligible patients with regional or distant stage cancer who had both resection and chemotherapy consistently had lower costs than their Medicare counterpar ts. Our assessment o\f colon cancer costs o\f \fers several insights. First, we provide �ne-tuned estimates o\f cancer costs during the �rst year \following diagnosis. Our method di\f \fers \from Wright et al. (2007) in that we explicitly model the zero cost outcome by a two-par t model HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 47

approach and compare the di\f \ferential cost between Medicare only and dually eligible patients. The regression method is a meaning\ful alter native to the phase-o\f-care method (Brown et al., 1999; Yabro\f \f et al., 2007a) and provides policy-relevant in\formation regarding cancer stage and treatment costs along with cost in\formation speci�c to patient char -acteristics such as age and comorbidity. Our method allows a prospective pre -diction o\f cancer cost by subpopulation, whereas the phase-o\f-care estimation depends on retrospectively segmenting sur vival into di\f \ferent periods and is not directly suitable \for predicting \future cost \for a given patient. Second, cancer treatment cost varies by stage o\f diagnosis and comorbid con -ditions. I\f recent screening initiatives are e\f \fective and result in \fewer cancer cases diagnosed at later stages, then the long-term costs o\f colon cancer will be lower. This has implications \for the longer-term \forecast o\f Medicare costs. Third, a recent study examining trends in the initial phase o\f cancer treatment \found that there were signi�cant in -creases in the propor tion o\f colorectal cancer patients undergoing chemo -therapy treatment and in the average Medicare payment \for those patients (War ren et al., 2008). This is consistent with our �ndings o\f signi�cantly higher costs \for patients with combined resection and chemotherapy. The cost o\f chemo -therapy will likely increase as newer and more expensive multidr ug chemotherapy regimens emerge. Our estimates ($29,196) \for cost attributable to colon cancer in 1 year are lower than the estimate in Yabro\f \f et al. (2007a) who \forecasted colorectal cancer cost \for the elderly (age 65 and above) by phase o\f care (the initial phase, the con -tinuing phase, and last year o\f li\fe) through the year 2020.8 The two estimates are not directly comparable in that the initial phase in Yabro\f \f et al. (2007a) does not include patients who sur vived less than 13 months. Our data included patients with sur vival within 12 months o\f diag -nosis \for whom the total cost was incur red in less than 1 year. In addition, the Yabro\f \f et al. (2007a) estimates include rectal cancer, which is more expensive to treat than colon cancer. We did not have data on skilled nursing home \facility, home health care or durable medical equipment costs. Mur ray and Eppig (1999) \found that 7 percent o\f Medicare bene�ciaries used a skilled or long-term care \facility in 1996 and home health care expenditure accounted \for 5 percent o\f the total Medicare expenditure. Excluding these claims led to an underestimate o\f the total cancer costs in our study. Finally our study \focused on patients undergoing surger y and chemotherapy and excluded patients who had radiation alone. Our aim was to estimate cancer costs by most typical treatment regimens. The review by Yabro\f \f et al. (2007b) iden -ti�ed the measurement o\f cost—payment, charges, or expenditures—as one ma\bor source o\f variation in cancer cost studies and lamented the lack o\f published stan -dards \for conducting and repor ting cost analyses. Brown et al. (2002) indicated Medicare payment was a good proxy \for the economic cost o\f medical ser vices compared to alter natives based on charges or cost-to-charge ratios. They relied on a “scale up” approach to account \for deduct -ibles and copayment when direct mea -sures were not available. We thus used the sum o\f Medicare payments, patient deductible and coinsurance and third-par ty payer paid amount as the measure o\f medical care costs, which is the most 8 The estimates were repor ted in 2002 dollars in Yabro\f \f et al. (2007a) whereas our estimates w\tere in 2000 dollars. HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 48

comprehensive and reliable measure o\f cost in the cur rent literature. Our study has several limitations. First, the study sample is con�ned to a single State and thus may not be generalizeable to other States or regions. However, that would only be the case i\f Michigan phy -sicians treated patients di\f \ferently than physicians treat patients in other States. Second, the study sample is speci�c to patients aged 65 years and older and may not be applicable to younger patients who may opt \for more aggressive treat -ments. Third, the sample does not include patients enrolled in a managed care plan; these patients may have a patter n o\f care that is di\f \ferent \from patients enrolled in a \fee-\for-ser vice plan. Never theless, the method we use can be applied to larger, nationwide datasets to estimate longer-term costs. Finally, our study period was \from 1997 to 2000, which precedes the Medicare Prescription Dr ug, Improve -ment and Moder nization Act o\f 2003 and as such does not contain any estimation o\f prescription dr ug costs \for the Medicare only group. To calculate total costs \for the dually eligible patients we did not include prescription payment because comparable in\formation was not avail -able \for Medicare only patients. It remains unknown i\f the Medicare only and dually eligible groups have di\f \ferent prescription costs in the Par t D era. We estimated the 1-year costs o\f colon cancer by stage, treatment, and patient characteristics such as comorbid con-ditions, age, and dual eligibility status. By incorporating these characteristics into our model, we can address ques-tions regarding the incremental costs o\f treating older patients, patients with advanced stage disease, patients with more comorbid conditions, and patients undergoing di\f \ferent treatment regimens. Finally, we applied a method o\f cost esti- mation to colon cancer that can be applied to larger national datasets \for a longer-term estimation o\f costs o\f cancer. Special considerations need to be given when a control patient develops cancer using methods similar to the nested case-control design (Barlow et al., 1999). This method complements methods that segment costs by disease stage and can be used prospec-tively \for cost prediction. As Medicare costs continue to grow, it is impor tant to under-stand the potential \factors that a\f \fect the pro\bection o\f \future costs. references Barlow, W. E., Ichikawa, L., Rosner, D., et al.: Analysis o\f Case-Cohor t Designs. Journal of Clinical Epi\femiology 52(12): 1165-1172, 1999. Basu, A. and Rathouz, P. J.: Estimating Marginal and Incremental E\f \fects on Health Outcomes Using Flexible Link and Variance Function Models. Biostatistics 6(1): 93-109, 2005. Bradley, C. J., Gardiner, J., Given, C. W., et al.: Cancer, Medicaid Enrollment, and Sur vival Disparities. Cancer 103(8): 1712-1718, 2005. Bradley, C. J., Given, C. W., Luo, Z., et al.: Medicaid, Medicare, and the Michigan Tumor Registr y: A Linkage Strategy. \be\fical Decision \baking 27(4): 352-363, 2007. Bradley, C. J., Luo, Z., and Given, C. W.: Cancer Incidence in Elderly Medicare and Dually Eligible Bene\ficiaries. Health Services Research 43(5): 1768-1778, 2008. Brown, M. L., Riley, G. F., Potosky, A. L., et al.: Obtaining Long-Term Disease Speci\fic Costs o\f Care—Application to Medicare Enrollees Diagnosed with Colorectal Cancer. \be\fical Care 37(12): 1249-1259, 1999. Brown, M. L., Riley, G. F., Schussler, N., et al.: Estimating Health Care Costs Related to Cancer Treatment \from SEER-Medicare Data. \be\fical Care 40(8): 104-117, 2002. Card, D. and Kr ueger, A. B.: Minimum-Wages and Employment—A Case-Study o\f the Fast-Food Industr y in New-Jersey and Pennsylvania. American Economic Review 84(4): 772-793, 1994. HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 49

Delco, F., Egger, R., Bauer \feind, P., et al.: Hospital Health Care Resource Utilization and Costs o\f Colorectal Cancer During the First 3-Year Period Following Diagnosis in Switzerland. Alimentar y Pharmacology & Therapeutics 21(5): 615-622, 2005. Deyo, R. A., Cherkin, D. C., and Ciol, M. A.: Adapting a Clinical Comorbidity Index \for Use with ICD-9-CM Administrative Databases. Journal of Clinical Epi\femiology 45: 613-619, 1992. Duan, N.: Smearing Estimate—A Nonparametric Retrans\formation Method. Journal of the American Statistical Association 78(383): 605-610, 1983. Etzioni, R., Riley, G. F., Ramsey, S. D., et al.: Measuring Costs—Administrative\t Claims Data, Clinical Trials, and Beyond. \be\fical Care 40(6): 63-72, 2002. Finkelstein, E. A., Bray, J. W., Chen, H., et al.: Prevalence and Costs o\f Ma\bor Depression among Elderly Claimants with Diabetes. Diabetes Care 26(2): 415-420, 2003. Gallo, J. J., Armenian, H. K., Ford, D. E., et al.: Ma\bor Depression and Cancer: The 13-Year Follow-Up o\f the Baltimore Epidemiologic Catchment Area Sample (United States). Cancer Causes Control 11(8): 751-758, 2000. Graubard, B. I. and Korn, E. L.: Predictive Margins with Sur vey Data. Biometrics 55(2): 652-659, 1999. Hershman, D., Hall, M. J., Wang, X., et al.: Timing o\f Ad\buvant Chemotherapy Initiation a\fter Surger y \for Stage III Colon Cancer. Cancer 107(11): 2581-2588, 2006. Klabunde, C. E., Potosky, A. L., Legler, J. M., et al.: Development o\f a Comorbidity Index Using Physician Claims Data. Journal of Clinical Epi\femiology 53: 1258-1267, 2000. Manning, W. G.: The Logged Dependent Variable, Heteroscedasticity, and the Retrans\formation Problem. Journal of Health Economics 17(3): 283-295, 1998. Manning, W. G., Basu, A. and Mullahy, J.: Generalized Modeling Approaches to Risk Ad\bustment o\f Skewed Outcomes Data. Journal of Health Economics 24(3): 465-488, 2005. Midgley, R. and Kerr, D. J.: Ad\buvant Chemotherapy \for Stage II Colorectal Cancer: The Time Is Right! Nature Clinical Practice. Oncology 2(7): 364-369, 2005. Mullahy, J.: Much Ado about Two: Reconsidering Retrans\formation and the Two-Par t Model in Health Econometrics. Journal of Health Economics 17(3): 247-281, 1998. Murray, L. A. and Eppig, F. J.: Health Expenditures \for Medicare Bene\ficiaries. Health Care Financing Review 21(2): 281-286, 1999. Murray, L. A. and Shatto, A. E.: Dually Eligible Medicare Bene\ficiaries. Health Care Financing Review 20(2): 131-140, 1998. Polsky, D., Doshi, J. A., Marcus, S., et al.: Long-Term Risk \for Depressive Symptoms a\fter a Medical Diagnosis. Archives of Internal \be\ficine 165(11): 1260-1266, 2005. Ward, E., Halpern, M., Schrag, N., et al.: Association o\f Insurance with Cancer Care Utilization and Outcomes. CA: A Cancer Journal for Clinicians 58(1): 9-31, 2008. Warren, J. L., Yabro\f \f, K. R., Meekins, A., et al.: Evaluation o\f Trends in the Cost o\f Initial Cancer Treatment. Journal of the National Cancer Institute 100(12): 888-987, 2008. Wright, G. E., Barlow, W. E., Green, P., et al.: Di\f \ferences among the Elderly in the Treatment Costs o\f Colorectal Cancer: How Impor tant Is Race? \be\fical Care 45(5): 420-430, 2007. Yabro\f \f, K. R., Mariotto, A. B., Feuer, et al.: Pro\bections o\f the Costs Associated with Colorectal Cancer Care in the United States, 2000-2020. Health Economics: Early View, 2007a. Yabro\f \f, K. R., Warren, J. L., and Brown, M. L.: Costs o\f Cancer Care in the USA: A Descriptive Review. Nature Clinical Practice. Oncology 4(11): 643-656, 2007b. Reprint Requests: Zh\tehui Luo, B601 West Fee Hall, Depar\t tment o\f Epidemiology, Michigan State Uni\tversity, East Lansing, MI 48824. E-mail: zluo\t@msu.edu HealtH Care FinanCing \0review/Fall 200\f/Volume 31, Number 1\t 50

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