Literature DB >> 10934539

Predictors of Medicare costs in elderly beneficiaries with breast, colorectal, lung, or prostate cancer.

L Penberthy1, S M Retchin, M K McDonald, D K McClish, C E Desch, G F Riley, T J Smith, B E Hillner, C J Newschaffer.   

Abstract

BACKGROUND: Determining the apportionment of costs of cancer care and identifying factors that predict costs are important for planning ethical resource allocation for cancer care, especially in markets where managed care has grown.
DESIGN: This study linked tumor registry data with Medicare administrative claims to determine the costs of care for breast, colorectal, lung and prostate cancers during the initial year subsequent to diagnosis, and to develop models to identify factors predicting costs.
SUBJECTS: Patients with a diagnosis of breast (n = 1,952), colorectal (n = 2,563), lung (n = 3,331) or prostate cancer (n = 3,179) diagnosed from 1985 through 1988.
RESULTS: The average costs during the initial treatment period were $12,141 (s.d. = $10,434) for breast cancer, $24,910 (s.d. = $14,870) for colorectal cancer, $21,351 (s.d. = $14,813) for lung cancer, and $14,361 (s.d. = $11,216) for prostate cancer. Using least squares regression analysis, factors significantly associated with cost included comorbidity, hospital length of stay, type of therapy, and ZIP level income for all four cancer sites. Access to health care resources was variably associated with costs of care. Total R2 ranged from 38% (prostate) to 49% (breast). The prediction error for the regression models ranged from < 1% to 4%, by cancer site.
CONCLUSIONS: Linking administrative claims with state tumor registry data can accurately predict costs of cancer care during the first year subsequent to diagnosis for cancer patients. Regression models using both data sources may be useful to health plans and providers and in determining appropriate prospective reimbursement for cancer, particularly with increasing HMO penetration and decreased ability to capture complete and accurate utilization and cost data on this population.

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Mesh:

Year:  1999        PMID: 10934539     DOI: 10.1023/a:1019096030306

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  26 in total

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Journal:  Am J Epidemiol       Date:  1997-02-01       Impact factor: 4.897

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Journal:  Breast Cancer Res Treat       Date:  1996       Impact factor: 4.872

8.  Potential for cancer related health services research using a linked Medicare-tumor registry database.

Authors:  A L Potosky; G F Riley; J D Lubitz; R M Mentnech; L G Kessler
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9.  Medicare payments from diagnosis to death for elderly cancer patients by stage at diagnosis.

Authors:  G F Riley; A L Potosky; J D Lubitz; L G Kessler
Journal:  Med Care       Date:  1995-08       Impact factor: 2.983

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  7 in total

1.  Differences in Medicare Expenditures Between Appalachian and Nationally Representative Cohorts of Elderly Women With Breast Cancer: An Application of Decomposition Technique.

Authors:  Ami Vyas; S Suresh Madhavan; Usha Sambamoorthi
Journal:  J Natl Compr Canc Netw       Date:  2017-05       Impact factor: 11.908

2.  Appraising the economic efficiency of cancer treatment: an exploratory analysis of lung cancer.

Authors:  Thomas N Chirikos
Journal:  Health Care Manag Sci       Date:  2003-05

3.  Healthcare Utilization and Costs During the Initial Phase of Care Among Elderly Women With Breast Cancer.

Authors:  Ami Vyas; S Suresh Madhavan; Usha Sambamoorthi; Xiaoyun Lucy Pan; Michael Regier; Hannah Hazard; Sita Kalidindi
Journal:  J Natl Compr Canc Netw       Date:  2017-11       Impact factor: 11.908

Review 4.  The costs of treating breast cancer in the US: a synthesis of published evidence.

Authors:  Jonathan D Campbell; Scott D Ramsey
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

Review 5.  Geriatric assessment in older patients with breast cancer.

Authors:  Heidi Klepin; Supriya Mohile; Arti Hurria
Journal:  J Natl Compr Canc Netw       Date:  2009-02       Impact factor: 11.908

6.  Comparison of hospital charge prediction models for colorectal cancer patients: neural network vs. decision tree models.

Authors:  Seung-Mi Lee; Jin-Oh Kang; Yong-Moo Suh
Journal:  J Korean Med Sci       Date:  2004-10       Impact factor: 2.153

7.  "Factors associated with non-small cell lung cancer treatment costs in a Brazilian public hospital".

Authors:  Carla de Barros Reis; Renata Erthal Knust; Claudia Cristina de Aguiar Pereira; Margareth Crisóstomo Portela
Journal:  BMC Health Serv Res       Date:  2018-02-17       Impact factor: 2.655

  7 in total

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