Literature DB >> 11405331

Finding incident breast cancer cases through US claims data and a state cancer registry.

P S Wang1, A M Walker, M T Tsuang, E J Orav, R Levin, J Avorn.   

Abstract

OBJECTIVE: With the increasing availability of automated health-care data, new methods are available to screen large populations for the presence of cancer diagnoses. However, it is crucial to evaluate how completely incident cancer cases can be ascertained using these data sources.
METHODS: We used capture-recapture techniques to estimate the total number of incident breast cancer cases occurring within one state during a 3-year period. We then compared the ascertainment of these cases by the following two data sources: claims for breast cancer surgery recorded in Medicaid and Medicare data vs a cancer registry in the same state.
RESULTS: Medicaid-Medicare breast cancer surgery claims identified 68% of the total estimated number of incident breast cancer cases while cancer registry data identified 78%. Case ascertainment improved markedly to 91% when both registry and Medicare-Medicaid data sources were used together. The sensitivity of ascertainment was lower for Medicaid-Medicare data among those aged under 65 and non-white; ascertainment was lower for the registry among women who were aged under 65, poor, and non-white.
CONCLUSIONS: Combining health insurance claims data with a population-based cancer registry improved the identification of incident cases of breast cancer, and may be particularly useful among demographic groups found to be at highest risk of under-ascertainment such as younger women, the poor, and racial minorities.

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Year:  2001        PMID: 11405331     DOI: 10.1023/a:1011204704153

Source DB:  PubMed          Journal:  Cancer Causes Control        ISSN: 0957-5243            Impact factor:   2.506


  9 in total

1.  Estimation of asthma incidence among low-income children in Texas: a novel approach using Medicaid claims data.

Authors:  Judy K Wendt; Elaine Symanski; Xianglin L Du
Journal:  Am J Epidemiol       Date:  2012-09-28       Impact factor: 4.897

2.  French claims data as a source of information to describe cancer incidence: predictive values of two identification methods of incident prostate cancers.

Authors:  Chantal Marie Couris; Arnaud Seigneurin; Sabiha Bouzbid; Muriel Rabilloud; Paul Perrin; Xavier Martin; Cyrille Colin; Anne-Marie Schott
Journal:  J Med Syst       Date:  2006-12       Impact factor: 4.460

3.  Improving primary care for older adults with cancer and depression.

Authors:  Jesse R Fann; Ming-Yu Fan; Jürgen Unützer
Journal:  J Gen Intern Med       Date:  2009-11       Impact factor: 5.128

4.  Leveraging Linkage of Cohort Studies With Administrative Claims Data to Identify Individuals With Cancer.

Authors:  Mackenzie R Bronson; Nirav S Kapadia; Andrea M Austin; Qianfei Wang; Diane Feskanich; Julie P W Bynum; Francine Grodstein; Anna N A Tosteson
Journal:  Med Care       Date:  2018-12       Impact factor: 2.983

5.  Is it possible to estimate the incidence of breast cancer from medico-administrative databases?

Authors:  L Remontet; N Mitton; C M Couris; J Iwaz; F Gomez; F Olive; S Polazzi; A M Schott; B Trombert; N Bossard; M Colonna
Journal:  Eur J Epidemiol       Date:  2008-08-21       Impact factor: 8.082

6.  Hospital discharge diagnostic and procedure codes for upper gastro-intestinal cancer: how accurate are they?

Authors:  Efty Stavrou; Nicole Pesa; Sallie-Anne Pearson
Journal:  BMC Health Serv Res       Date:  2012-09-21       Impact factor: 2.655

7.  In the absence of cancer registry data, is it sensible to assess incidence using hospital separation records?

Authors:  Moyra E Brackley; Margaret J Penning; Mary L Lesperance
Journal:  Int J Equity Health       Date:  2006-10-06

8.  Using hospital discharge data to identify incident pregnancy-associated cancers: a validation study.

Authors:  Yuen Yi Cathy Lee; Christine L Roberts; Jane Young; Timothy Dobbins
Journal:  BMC Pregnancy Childbirth       Date:  2013-02-11       Impact factor: 3.007

9.  Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review.

Authors:  Iosief Abraha; Alessandro Montedori; Diego Serraino; Massimiliano Orso; Gianni Giovannini; Valeria Scotti; Annalisa Granata; Francesco Cozzolino; Mario Fusco; Ettore Bidoli
Journal:  BMJ Open       Date:  2018-07-23       Impact factor: 2.692

  9 in total

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