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Treatment of Locally Advanced Pancreatic Cancer in the Real World: Population-Based Practices and Effectiveness

Monika K. Krzyzanowska, Jane C. Weeks, Craig C. Earle

From the Center for Outcomes and Policy Research, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston, MA.

Address reprint requests to Craig C. Earle, MD, Center for Outcomes and Policy Research, Dana-Farber Cancer Institute, 44 Binney St, 454-STE 21–24, Boston, MA 02115; email: craig_earle{at}dfci.harvard.edu.

	ABSTRACT

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Purpose: To evaluate the use and effectiveness of cancer-directed therapy in elderly patients with locally advanced pancreatic cancer (LAPC).

Methods: We used the linked Surveillance, Epidemiology, and End Results Medicare database to perform a retrospective cohort study in 1,696 patients diagnosed with LAPC between 1991 and 1996. We calculated cancer-directed treatment use rates, then used logistic regression to identify patient and health system factors that were associated with receipt of treatment. Effectiveness of treatment was estimated using Cox proportional hazards models and propensity score methods.

Results: In our cohort, 44% of patients received some form of cancer-directed therapy (24% radiation with concurrent chemotherapy, 13% radiation alone, and 7% chemotherapy alone). Older age, lower socioeconomic status, presence of comorbid illness, no care in a teaching hospital, and residence in the western United States were associated with a lower likelihood of receiving treatment (P .05). Among those treated, younger age and certain geographic locations were the only predictors of receiving combined-modality therapy. The adjusted hazard ratio for death associated with any treatment in the Cox model was 0.53 (P < .0001). Effectiveness estimates obtained using propensity score methods were similar.

Conclusion: This analysis supports the effectiveness of cancer-directed treatment in elderly patients with LAPC, but use is low. Receipt of treatment is strongly correlated with non–disease-related factors, especially sociodemographic characteristics, indicating possible disparities in access to care.

	INTRODUCTION

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PANCREATIC CANCER is the fourth leading cause of cancer-related mortality in men and women in the United States.¹ Surgery is the only potentially curative option, but less than 20% of patients with pancreatic cancer are candidates for resection. For individuals who present with locally advanced disease, radiation with concurrent fluorouracil-based chemotherapy has long been the mainstay of treatment. A series of small, randomized trials conducted between the 1960s and 1980s showed a significant survival advantage for combined-modality therapy compared with radiation alone.^2,3 More recently, single-agent gemcitabine has become a therapeutic option for some patients, especially those with poor performance status. In a trial that compared gemcitabine to fluorouracil and included patients with locally advanced as well as metastatic disease, treatment with gemcitabine was associated with an improvement in quality of life and prolonged survival.⁴

Rates and determinants of use of these treatments among patients with locally advanced pancreatic cancer are not well characterized. Furthermore, it is unclear whether the treatment efficacy observed in clinical trials can be realized in actual practice because the majority of patients with pancreatic cancer are older and sicker than were the participants of the randomized trials. To address these issues, we used the linked Surveillance, Epidemiology, and End Results (SEER) Medicare database to conduct a retrospective cohort study that examined the patterns of care and effectiveness of cancer-directed therapy in elderly patients with locally advanced pancreatic cancer.

	METHODS

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Data Sources
We used data from the SEER program sponsored by the National Cancer Institute to identify patients for this study. This program is made up of 11 tumor registries covering approximately 14% of the United States population.⁵ Each registry collects information on all newly diagnosed cancer patients in a defined region, including date of diagnosis, primary site, histologic type, stage at diagnosis, date and cause of death, initial cancer-directed surgery or radiation, and information on patient sociodemographic characteristics.

The SEER database has been linked to the Centers for Medicare and Medicaid Services (formerly the Health Care Financing Administration) Medicare database through to the end of 1998, and to census-tract level socioeconomic data. The Medicare program offers health insurance to more than 95% of the United States population aged 65 years and older. Medicare files capture information on inpatient as well as outpatient procedures, physician and laboratory billings, and bills for home health and hospice care. Each patient in the SEER and Medicare databases has a unique case identification number that has permitted matching and merging of the different files with a 94% match rate.⁵

Cohort Definition
Our study population consisted of all Medicare-eligible patients who resided in one of the SEER areas between January 1, 1991 and December 31, 1996, and who were diagnosed with pathologically confirmed locally advanced adenocarcinoma of the pancreas. Patients were considered to have locally advanced disease if they presented with nonmetastatic cancer but did not undergo potentially curative cancer-directed surgery defined as either a Whipple procedure or a distal pancreatectomy. Surgery was identified from the Medicare database using the International Classification of Diseases, 9th Revision (ICD-9; codes 52.51 to 52.53, 52.56, 52.57, 52.59, 52.6, and 52.7).⁶ We excluded patients who were enrolled in a Health Maintenance Organization and those ineligible for either part of Medicare because complete billing information would not be available for them. We also excluded patients for whom the date of death differed by more than 2 months between the SEER and Medicare databases, patients who were diagnosed from death certificate or autopsy, patients who were eligible for Medicare on the basis of end-stage renal failure, and patients for whom either the month of diagnosis or the stage at diagnosis were not available.

Identification of Radiation and Chemotherapy Use
Information on radiation use is available in SEER records and from Medicare data. In SEER, this information is coded as cancer-directed radiation and corresponds to radiation given within the first 4 months after diagnosis. To identify receipt of radiation therapy in the Medicare files, we used an algorithm that combined ICD-9 diagnosis (V58.0, V66.1, V67.1) and procedure codes (92.20 to 92.29) with Health Care Financing Administration’s Common Procedure (77401 to 77499 and 77750 to 77799), Diagnosis-Related Group 409, and revenue center codes (0330, 0333, and 0339). We assumed that a patient had radiation if he or she was identified as having radiation according to either source within the first 4 months after diagnosis.

Information on chemotherapy use is only available in the Medicare database. We developed an algorithm similar to that for identification of radiation therapy to identify chemotherapy use during the first 4 months after diagnosis (ICD-9 codes V58.1, V66.2, V67.2, E933.1, and E930.7, and procedure code 99.25; and Health Care Financing Administration’s Common Procedure codes Q0083 to Q0085, J7150, J8999, J9XXX, 964XX, and 965XX; Diagnosis-Related Group 410; and revenue center codes 0331, 0332, and 0335). Patients were considered to have had concurrent, combined-modality therapy if the dates for radiation and chemotherapy overlapped by 1 month. For the small subset of patients (n = 127) who had radiation only noted in SEER, which does not record exact treatment dates, we assumed that they received concurrent therapy if they also received chemotherapy any time during the first 4 months after diagnosis.

Definition of Patient and Health System Characteristics
The information available in the linked database was used to classify patients by age at diagnosis, sex, race or ethnicity (white, black, or other), year of diagnosis, and socioeconomic status (SES). SES was determined using the median income in the patient’s zip code of residence as a proxy, and was subdivided into quintiles. Patients were also classified geographically on the basis of registry. We grouped the registries into the four census regions: Northeast, Midwest, South, and West.

To examine treatment variation on the basis of severity of noncancer illness, both inpatient and outpatient data were used. For the inpatient data, we calculated the Charlson comorbidity score for each patient by examining the ICD-9 diagnostic codes recorded in months -1 to -13 before the diagnosis of pancreatic cancer using the method described by Deyo et al.⁷ To increase the sensitivity of the analysis, we also examined outpatient bills, as recommended by Klabunde et al.⁸

Patients were classified as having been treated at a teaching hospital if their record contained at least one bill that included a charge for medical education. To assess local availability of care, we determined the number of facilities with oncology services and the availability of radiation oncologists per 10,000 population by health service area using the Area Resource File⁹ maintained by the Federal Health Resources and Services Administration. For analysis, both of these variables were divided into tertiles.

Effectiveness of Treatment
Survival was calculated from the date of diagnosis until either death or the last date for which study data were collected (December 31, 1998). Effectiveness was estimated separately for each of the following patient groups: radiation plus chemotherapy, radiation alone, chemotherapy alone, and no treatment.

Statistical Methods
To assess the association between patient and health system characteristics and the receipt of treatment, we compared frequencies using the ² test of significance. Variables examined included age, sex, race or ethinicity, SES, year of diagnosis, region, residence in an urban area, Charlson comorbidity score, stage, receipt of care in teaching hospital, and the local availability of hospitals with oncology services and radiation oncologists. Variables that were significant in univariate analyses (P .05) were entered into a multivariable, stepwise logistic regression model in which the outcome of interest was receipt of any cancer-directed treatment. A second regression model was developed to identify factors associated with receipt of combined-modality therapy among patients who received treatment. If meaningful interactions between variables were identified, they were incorporated into the final models.

To evaluate treatment effectiveness, we used Cox modeling techniques and propensity scores. Estimates of the survival associated with different treatment modalities adjusted for age, sex, and comorbidity were obtained from a Cox model. Propensity score methods were developed to minimize selection bias when estimating effectiveness of interventions from observational data.¹⁰ Logistic regression was used to determine each patient’s propensity for receiving treatment. The propensity scores were then used to divide the cohort into five groups and effectiveness of treatment relative to no therapy was estimated within the quintiles. Statistical Analysis Software Version 8.1 (SAS Institute, Cary, NC) was used for all analyses.

	RESULTS

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Study Population
During the study period, 2,259 patients were diagnosed with locally advanced pancreatic cancer while living in regions monitored by the SEER program. Among this cohort, the diagnosis of cancer was pathologically confirmed in 1,696 individuals (75%). Subsequent analyses were limited to this patient group, whose characteristics are summarized in Table 1. The median age of the cohort was 74.8 years and most patients were white. The majority of patients had no or at most one comorbid condition. The most common comorbidities were diabetes (22%), chronic pulmonary disease (8%), and congestive heart failure (6%). Approximately half of the patients resided in western United States and 86% lived in an urban area.

Factors Associated With Receipt of Treatment
In univariate analyses, all examined factors except race or ethinicity, year of diagnosis, stage, and residence in a rural area were significantly (P .05) associated with the receipt of treatment (presented as part of Table 1). Younger patients, men, patients with higher SES, and those with fewer comorbid conditions were more likely to receive treatment. For example, 59% of patients aged 65 to 69 years received treatment, whereas only 12% of patients aged 85 years or older received therapy. In the lowest socioeconomic stratum, about one third of patients received treatment; in contrast, more than one half of the patients in the top socioeconomic stratum received treatment. Significant variation was also seen across different regions of the United States, with lowest use rates in the West. Treatment rates increased with greater supply of both the number of hospitals with oncology services and the number of radiation oncologists.

The only factors that remained significant predictors of receipt of treatment in the multivariable model were age, SES, presence of comorbid illness, receipt of care in a teaching hospital, and place of residence (Table 2). Directions of these associations were the same as in the univariate analyses. Age had the most significant effect (P < .0001), with odds of 0.43 for receiving treatment with each decade increase in age. The geographic variation in treatment seen in univariate analyses persisted even after adjustment for age, SES, and number of comorbid conditions.

Treatment Effectiveness
Survival curves for treated and untreated patients adjusted for age, sex, and comorbidity are presented in Figure 1. Results are shown for a patient with the median characteristics of the cohort. The adjusted median survivals in the four groups were 47 weeks (radiation plus chemotherapy), 29 weeks (radiation alone), 27 weeks (chemotherapy alone), and 15 weeks (no treatment).

View larger version (14K): [in this window] [in a new window]   Fig 1. Survival adjusted for age, sex, and comorbidity for patients receiving treatment versus untreated patients.

The estimated propensity for receiving treatment ranged from 4% to 85%, with overlap between the treated and untreated groups. As seen in Table 3, the distribution of baseline characteristics between treated and untreated patients within each propensity quintile was well balanced. Effectiveness estimates using propensity methods are summarized in Table 4. The estimates were similar to those obtained using Cox modeling techniques.

	DISCUSSION

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Compared with surgical treatment for localized disease,^13–17 the processes and outcomes of nonsurgical therapy for pancreatic cancer have received little attention. The National Cancer DataBase (NCDB), a project commissioned by the American College of Surgeons, has been the primary source of data on treatment practices for advanced pancreatic cancer.^18–20 The NCDB collects data on cancer diagnoses and treatment from hospital registries throughout the country. The rates of cancer-directed treatment among patients with nonmetastatic disease who did not undergo surgery have varied from 39% to 45% in the NCDB studies, with 21% to 27% of patients receiving combined-modality therapy. Despite differences between the NCDB population and our cohort, especially with respect to age distribution and insurance coverage, the NCDB rates are remarkably similar to those observed in this study, indicating that treatment use is low in general among patients with locally advanced pancreatic cancer.

Given that therapy for locally advanced pancreatic cancer is not curative and may be associated with treatment-related morbidity, it is fair to ask whether the low rates of use we observed are cause for concern. The answer depends on whether treatment prolongs survival, whether the benefit is seen in all patients rather than selected subsets, and, on average, whether the benefits (in terms of survival and palliation) outweigh the treatment-related toxicity. Prior clinical trials have failed to provide definitive answers to these questions. Combined-modality therapy has been associated with longer survival relative to radiation alone in randomized trials,^2,3 but whether these benefits can be realized outside of the trial setting has not been clear. Moreover, the relative value of treatment compared with no treatment is not known because none of the randomized trials included a no-treatment arm.

To address these issues, we estimated the effectiveness of treatment in our cohort. Because of concerns regarding selection bias when observational data are used to estimate treatment effects, we used several statistical techniques including propensity scores methods to assess effectiveness. In propensity score analysis we are not looking for trends from lowest to highest propensity group, but rather evidence of treatment benefit even among patients with the lowest propensity to be treated. The HRs for death associated with treatment in our cohort were consistent across propensity groups and were similar to results obtained using Cox regression. Because none of the trials have compared treatment to no treatment, we cannot directly compare the hazard ratios observed in this study with those from randomized trials. However, the adjusted median survival of patients treated with combined-modality therapy in our cohort was 47 weeks and is similar to the median survival of patients treated with radiation plus chemotherapy in randomized trials, which has ranged from 36 to 49 weeks.^2,3,11,12 Although we cannot address the question whether treatment palliates, our findings suggest that treatment for this disease results in meaningful increases in length of life in the routine care setting.

The use of treatment in our cohort was associated with several non–disease-related factors. Similar findings have been documented in other malignancies, both in relation to treatments with curative intent^21–25 and for treatments that prolong survival or improve quality of life without being curative.^26,27 Although lower use rates in older patients may in part reflect appropriate consideration of patient preferences and functional status, the extent of age-related variation in treatment seen in this study argues against these being the only reasons. Referral bias in the treatment of advanced lung cancer was reported in a recent study,²⁸ suggesting that physician attitudes may contribute to this problem and should be explored further. The interplay between SES and receipt of medical care is complex.²⁹ Differences in care related to SES may reflect different attitudes toward health or logistic issues with access to care. If logistic issues, such as lack of financial resources to attend appointments, were the main obstacle in our study, we would expect SES not only to be a determinant of receipt of treatment, but also of the type of treatment received. Because radiation-based treatment requires the largest number of visits, we would expect patients from lower socioeconomic strata to be more likely to receive chemotherapy, which we did not observe. More likely, SES operates at the level of entry into the healthcare system. The finding that SES is a determinant of treatment even among patients with uniform insurance suggests that nonmedical, patient-specific factors are among the greatest barriers to quality cancer care.

Our study is limited by the constraints of administrative data, especially the lack of information on patient preferences, functional status, neighborhood healthcare resources, and physician biases. Because of the observational nature of our study and the lack of detailed information on performance status, our results regarding the effectiveness of treatment versus no treatment in pancreatic cancer are encouraging but not definitive. Such evidence can best come from well-designed, prospective, randomized trials that include a no-treatment comparison. In the meantime, pancreatic cancer patients and their physicians are left to make treatment decisions for this difficult disease with the data available. Lastly, all of the patients in our cohort were Medicare enrollees, thus the findings might be different in other healthcare systems or in younger patients. However, because approximately 70% of patients diagnosed with pancreatic cancer are 65 years of age or older,³⁰ Medicare represents the primary form of insurance for a substantial proportion of patients with pancreatic cancer.

Although it is not possible to determine the optimal proportion of patients that should receive combined-modality therapy for locally advanced pancreatic cancer, there is little reason to believe that patients living in different parts of the country or with different SES would make markedly different treatment choices if they were presented with the same information and opportunities. Rather, the disparities observed here suggest that locally advanced pancreatic cancer is another disease for which unacceptable barriers to care exist. Recent studies suggest that differences in outcome between patients may be due to differences in care rather than biologic variation,^31,32 which further emphasizes the importance of equal access to quality care for all. The time has come to concentrate our efforts on developing effective interventions and policies that can overcome the disparities in care received by many of the marginalized groups in our society.

	AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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The authors indicated no potential conflicts of interest.

	NOTES

 
Monika K. Krzyzanowska, MD, is the recipient of a Cancer Care Ontario Fellowship.

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Submitted March 3, 2003; accepted June 18, 2003.

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This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Krzyzanowska, M. K. Articles by Earle, C. C. Search for Related Content PubMed PubMed Citation Articles by Krzyzanowska, M. K. Articles by Earle, C. C. Social Bookmarking                            What's this?