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Oncology: Original Articles

Healthcare costs, treatment patterns, and resource utilization among pancreatic cancer patients in a managed care population

, , , &
Pages 1379-1386 | Accepted 20 Sep 2013, Published online: 18 Oct 2013
 

Abstract

Background:

Pancreatic adenocarcinoma has few effective treatment options and poor survival. The objective of this study was to characterize treatment patterns and estimate the costs and resource use associated with its treatment in a commercially-insured US population.

Methods:

In this retrospective claims-based analysis, individuals ≥18 years old with evidence of pancreatic adenocarcinoma between January 1, 2001 and December 31, 2010 were selected from a managed care database. Treatment phase (either initial non-metastatic or metastatic) was determined using a claims-based algorithm. Patients in the pancreatic cancer population were matched 1:3 to a control population. Resource use (events/person-years), treatment patterns, and healthcare costs (per-patient per-month, PPPM) were determined during a variable length follow-up period (from first pancreatic cancer diagnosis to earliest of death, disenrollment, or study end).

Results:

In this study, 5262 pancreatic cancer patients were matched to 15,786 controls. Rates of office visits, inpatient visits, ER visits, and inpatient stays, and mean total all-cause healthcare costs PPPM ($15,480 vs $1001) were significantly higher among cancer patients than controls (all p < 0.001). Mean inpatient costs were the single largest cost driver ($9917 PPPM). Also, mean total all-cause healthcare costs were significantly higher during the metastatic treatment phase vs the initial treatment phase of non-metastatic disease ($21,637 vs $10,358, p < 0.001).

Conclusions:

These results indicate that pancreatic cancer imposes a substantial burden on the US healthcare system, and that treatment of more advanced disease is significantly more costly than initial treatment of non-metastatic disease.

Limitations:

Additional research is needed to validate the accuracy of the claims-based algorithms used to identify the treatment phase.

Transparency

Declaration of funding

This study was sponsored by Eli Lilly and Company.

Declaration of financial/other interests

Emily Nash Smyth, Daniel Mytelka, and Lee Bowman are all employees and shareholders of Eli Lilly and Company. Stacey DaCosta Byfield and April Teitelbaum have disclosed that they work for OptumInsight, a company that received funding from Eli Lilly for its role in the development on this study. JME Peer Reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgments

The authors thank Jesse Potash, PhD, at OptumInsight for assistance with preparation of this manuscript.

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