Surgical resection is the primary treatment for colon cancer, but use of laparoscopic approaches varies widely despite demonstrated short- and long-term benefits.
The purpose of this study was to identify characteristics associated with laparoscopic colon cancer resection and to quantify variation based on patient, hospital, and geographic characteristics.
Bayesian cross-classified, multilevel logistic models calculated adjusted ORs and CIs for patient, surgeon, hospital, and geographic characteristics and unexplained variability (predicted vs. observed values) using adjusted median odds ratios for hospitals and counties.
The Surveillance, Epidemiology, and End Results–Medicare claims database (2008–2011) supplemented with county-level American Community Survey (2008–2012) demographic data was used.
A total of 10,618 patients ≥66 years old who underwent colon cancer resection were included.
Nonurgent/nonemergent resections for colon cancer patients ≥66 years old were classified as laparoscopic or open procedures.
Patients resided in 579 counties and used 950 hospitals; 47% of patients underwent laparoscopic surgery. Medicare/Medicaid dual enrollment, age ≥85 years, and higher tumor stage and grade were negatively associated with laparoscopic surgery receipt; proximal tumors and increasing hospital size and surgeon caseload were positively associated. Significant unexplained variability at the hospital (adjusted median OR = 3.31; p < 0.001) and county levels (adjusted median OR = 1.28; p < 0.05) remained after adjustment.
This was an observational study lacking generalizability to younger patients without Medicare or those with Health Maintenance Organization coverage and data set did not reflect national hospital studies or hospital volume. In addition, we were unable to account for specific types of comorbidities, such as obesity, and had broad categories for surgeon caseload.
Determining sources of hospital-level variation among poor insured patients may help increase laparoscopic resection to maximize health outcomes and reduce cost. See Video Abstract at http://links.lww.com/DCR/A363.
1 Department of Epidemiology, Saint Louis University, St. Louis, Missouri
2 Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
3 Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
4 Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina
5 Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, South Carolina
6 South Carolina Rural Health Research Center, University of South Carolina, Columbia, South Carolina
7 Department of Clinical Sciences and Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas
Supplemental digital content is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML and PDF versions of this article on the journal’s Web site (www.dcrjournal.com).
Funding/Support: This work was supported in part by grants from the National Institutes of Health National Cancer Institute: P30 CA091842 (to Dr. Eberlain), K07 CA178331 (to Dr. Lian), R21 CA169807 (to Dr. Lian), R56 AG049503 (to Dr. Schootman), and R01 CA137750 (to Dr. Schootman). Dr. Davidson was supported in part through grants HL-38180, DK-56260, and Digestive Disease Research Core Center grant DK-52574. Dr. Eberth was supported in part by a Mentored Research Scholar Grant (MRSG-15-148-01-CPHPS) from the American Cancer Society. Dr. Jeffe was supported in part by the National Cancer Institute Cancer Center Support Grant to the Siteman Cancer Center (P30 CA091842).
Financial Disclosure: None reported.
Presented at the Conference on Geospatial Approaches to Cancer Control and Population Sciences, Bethesda, MD, September 12 to 14, 2016.
Correspondence: Kendra L. Ratnapradipa, M.S.W., 105 Magnolia Way, Huntsville, TX 77320. E-mail: email@example.com