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Title: Survival analyses of postoperative lung cancer patients: an investigation using Japanese administrative data.
Authors: Kunisawa, Susumu
Yamashita, Kazuto
Ikai, Hiroshi
Otsubo, Tetsuya  kyouindb  KAKEN_id
Imanaka, Yuichi  kyouindb  KAKEN_id
Author's alias: 國澤, 進
今中, 雄一
Keywords: Lung cancer
Survival analysis
Administrative data
Japan
Issue Date: 1-May-2014
Publisher: Springer Open
Journal title: SpringerPlus
Volume: 3
Thesis number: 217
Abstract: Long-term survival rates of cancer patients represent important information for policymakers and providers, but analyses from voluntary cancer registries in Japan may not reflect the overall situation. In 2003, the Diagnosis Procedure Combination Per-Diem Payment System (DPC/PDPS) for hospital reimbursement was introduced in Japan; more than half of Japan's acute care beds are currently covered under this system. Administrative data produced under the DPC system include claims data and clinical summaries for each admission. Due to the large amount of data spanning multiple institutions, this database may have applications in providing a more general and inclusive overview of healthcare. Here, we investigate the use of administrative data for analyses of long-term survival in cancer patients. We analyzed postoperative survival in 7,064 patients with primary non-small cell lung cancer admitted to 102 hospitals between April 2008 and March 2013 using DPC data. Survival was defined at the last date of examination or discharge within the study period, and the event was mortality during the same period. Overall survival rates for different cancer stages were calculated using the Kaplan-Meier method. Additionally, survival rates of cancer patients at clinical stage IA were compared between low- and high-volume hospitals using the Log-rank test. Postoperative 5-year survival for patients at stage IA was 85.8% (95% CI = 78.6%-93.0%). High-volume hospitals had higher survival rates than hospitals with lower volume. Our findings using large-scale administrative data were similar to previous clinical registry reports, showing potential applications as a new method in analyzing up-to-date healthcare information.
Rights: © 2014 Kunisawa et al.; licensee Springer.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
URI: http://hdl.handle.net/2433/187118
DOI(Published Version): 10.1186/2193-1801-3-217
PubMed ID: 24826376
Appears in Collections:Journal Articles

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