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Current Oncology
Volume 26
Issue 5
10.3747/co.26.5279
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Current Oncology is published by MDPI from Volume 28 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Multimed Inc..
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Short Communication

The Generation of Two Specific Cancer Costing Algorithms Using Ontario Administrative Databases

by
Nicole Mittmann
1,2,*,
S. Y. Cheng
3,
N. Liu
3,
S. J. Seung
4,
F. E. Saxena
3,
C. DeAngelis
5,
N. J. Look Hong
5,
C. C. Earle
1,3,5,
M. C. Cheung
5,
N. Leighl
6,
N. Coburn
3,5 and
W. K. Evans
7
1
Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, 2075 Bayview Ave, E240, Toronto, ON M4N 3M5, Canada
2
Canc Care Ontario, Toronto, ON, Canada
3
ICES, Toronto, ON, Canada
4
Health Outcomes and PharmacoEconomic (HOPE) Research Centre, Toronto, ON, Canada
5
Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
6
University Health Network, Toronto, ON, Canada
7
McMaster University, Hamilton, ON, Canada
*
Author to whom correspondence should be addressed.
Curr. Oncol. 2019, 26(5), 682-692; https://doi.org/10.3747/co.26.5279
Submission received: 10 July 2019 / Revised: 5 August 2019 / Accepted: 2 September 2019 / Published: 1 October 2019

Abstract

Cancer treatment and management have become increasingly economically burdensome. Consequently, to help with planning health service delivery, it is vital to understand the associated costs. Administrative databases can be used to help understand and generate real-world system-level costs. Using databases to generate costs can take one of two approaches: top-down or bottom-up. Top-down approaches disaggregate the total health care spending from a global health care budget by sector and provider. A bottom-up approach begins with individual-level health care use and its costs, which are then aggregated.
Keywords: Costing algorithms Costing algorithms

Share and Cite

MDPI and ACS Style

Mittmann, N.; Cheng, S.Y.; Liu, N.; Seung, S.J.; Saxena, F.E.; DeAngelis, C.; Hong, N.J.L.; Earle, C.C.; Cheung, M.C.; Leighl, N.; et al. The Generation of Two Specific Cancer Costing Algorithms Using Ontario Administrative Databases. Curr. Oncol. 2019, 26, 682-692. https://doi.org/10.3747/co.26.5279

AMA Style

Mittmann N, Cheng SY, Liu N, Seung SJ, Saxena FE, DeAngelis C, Hong NJL, Earle CC, Cheung MC, Leighl N, et al. The Generation of Two Specific Cancer Costing Algorithms Using Ontario Administrative Databases. Current Oncology. 2019; 26(5):682-692. https://doi.org/10.3747/co.26.5279

Chicago/Turabian Style

Mittmann, Nicole, S. Y. Cheng, N. Liu, S. J. Seung, F. E. Saxena, C. DeAngelis, N. J. Look Hong, C. C. Earle, M. C. Cheung, N. Leighl, and et al. 2019. "The Generation of Two Specific Cancer Costing Algorithms Using Ontario Administrative Databases" Current Oncology 26, no. 5: 682-692. https://doi.org/10.3747/co.26.5279

APA Style

Mittmann, N., Cheng, S. Y., Liu, N., Seung, S. J., Saxena, F. E., DeAngelis, C., Hong, N. J. L., Earle, C. C., Cheung, M. C., Leighl, N., Coburn, N., & Evans, W. K. (2019). The Generation of Two Specific Cancer Costing Algorithms Using Ontario Administrative Databases. Current Oncology, 26(5), 682-692. https://doi.org/10.3747/co.26.5279

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