Literature DB >> 34737206

Validation of Self-reported Cancer Diagnoses Using Medicare Diagnostic Claims in the US Health and Retirement Study, 2000-2016.

Megan A Mullins1,2, Jasdeep S Kler3, Marisa R Eastman3, Mohammed Kabeto4, Lauren P Wallner2,3,4, Lindsay C Kobayashi2,3.   

Abstract

BACKGROUND: The US Health Retirement Study (HRS) is an ongoing population-representative cohort of US adults ages >50 with rich data on health during aging. Self-reported cancer diagnoses have been collected since 1998, but they have not been validated. We compared self-reported cancer diagnoses in HRS interviews against diagnostic claims from linked Medicare records.
METHODS: Using HRS-Medicare linked data, we examined the validity of first incident cancer diagnoses self-reported in biennial interviews from 2000 to 2016 against ICD-9 and ICD-10 diagnostic claim records as the gold standard. Data were from 8,242 HRS participants ages ≥65 with 90% continuous enrollment in fee-for-service Medicare. We calculated the sensitivity, specificity, and κ for first incident invasive cancer diagnoses (all cancers combined, and each of bladder, breast, colorectal/anal, uterine, kidney, lung, and prostate cancers) cumulatively over the follow-up and at each biennial study interview.
RESULTS: Overall, self-reports of first incident cancer diagnoses from 2000 to 2016 had 73.2% sensitivity and 96.2% specificity against Medicare claims (κ = 0.73). For specific cancer types, sensitivities ranged from 44.7% (kidney) to 75.0% (breast), and specificities ranged from 99.2% (prostate) and 99.9% (bladder, uterine, and kidney). Results were similar in sensitivity analyses restricted to individuals with 100% continuous fee-for-service Medicare enrollment and when restricted to individuals with at least 24 months of Medicare enrollment.
CONCLUSIONS: Self-reported cancer diagnoses in the HRS have reasonable validity for use in population-based research that is maximized with linkage to Medicare. IMPACT: These findings inform the use of the HRS for population-based cancer and aging research. ©2021 American Association for Cancer Research.

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Year:  2021        PMID: 34737206      PMCID: PMC8755623          DOI: 10.1158/1055-9965.EPI-21-0835

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.090


  22 in total

1.  Overview of the SEER-Medicare data: content, research applications, and generalizability to the United States elderly population.

Authors:  Joan L Warren; Carrie N Klabunde; Deborah Schrag; Peter B Bach; Gerald F Riley
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2.  The relationship between health information sources and mental models of cancer: findings from the 2005 Health Information National Trends Survey.

Authors:  Edith Kealey; Cathy S Berkman
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3.  Validating Reports of Chronic Conditions in the Medicare CAHPS Survey.

Authors:  Matthew W Brault; Bruce E Landon; Alan M Zaslavsky
Journal:  Med Care       Date:  2019-10       Impact factor: 2.983

4.  Cohort Profile: the Health and Retirement Study (HRS).

Authors:  Amanda Sonnega; Jessica D Faul; Mary Beth Ofstedal; Kenneth M Langa; John W R Phillips; David R Weir
Journal:  Int J Epidemiol       Date:  2014-03-25       Impact factor: 7.196

5.  Cancer treatment and survivorship statistics, 2019.

Authors:  Kimberly D Miller; Leticia Nogueira; Angela B Mariotto; Julia H Rowland; K Robin Yabroff; Catherine M Alfano; Ahmedin Jemal; Joan L Kramer; Rebecca L Siegel
Journal:  CA Cancer J Clin       Date:  2019-06-11       Impact factor: 508.702

6.  Validity of self-reported cancer: Comparison between self-report versus cancer registry records in the Geelong Osteoporosis Study.

Authors:  Stephanie P Cowdery; Amanda L Stuart; Julie A Pasco; Michael Berk; David Campbell; Lana J Williams
Journal:  Cancer Epidemiol       Date:  2020-07-31       Impact factor: 2.984

7.  Evaluation of three algorithms to identify incident breast cancer in Medicare claims data.

Authors:  Heather T Gold; Huong T Do
Journal:  Health Serv Res       Date:  2007-10       Impact factor: 3.402

8.  The validity of self-reported cancer in an Australian population study.

Authors:  Venurs Loh; Jessica Harding; Vira Koshkina; Elizabeth Barr; Jonathan Shaw; Dianna Magliano
Journal:  Aust N Z J Public Health       Date:  2014-02       Impact factor: 2.939

9.  Making headlines: an analysis of US government-funded cancer research mentioned in online media.

Authors:  Lauren A Maggio; Chelsea L Ratcliff; Melinda Krakow; Laura L Moorhead; Asura Enkhbayar; Juan Pablo Alperin
Journal:  BMJ Open       Date:  2019-02-19       Impact factor: 2.692

10.  Interrater reliability: the kappa statistic.

Authors:  Mary L McHugh
Journal:  Biochem Med (Zagreb)       Date:  2012       Impact factor: 2.313

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  2 in total

1.  Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study.

Authors:  Ramya Ramasubramanian; Helen C S Meier; Sithara Vivek; Eric Klopack; Eileen M Crimmins; Jessica Faul; Janko Nikolich-Žugich; Bharat Thyagarajan
Journal:  Immun Ageing       Date:  2022-07-20       Impact factor: 9.701

2.  Functional aging trajectories of older cancer survivors: a latent growth analysis of the US Health and Retirement Study.

Authors:  Ashly C Westrick; Kenneth M Langa; Marisa Eastman; Monica Ospina-Romero; Megan A Mullins; Lindsay C Kobayashi
Journal:  J Cancer Surviv       Date:  2022-02-26       Impact factor: 4.062

  2 in total

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