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

Bibliographic Data

ID9101762
AuthorsMackenzie R Bronson (Dartmouth Institute for Health Policy and Clinical Practice), Mackenzie Bronson (Dartmouth Institute for Health Policy and Clinical Practice, corresponding author), Nirav S Kapadia (0000-0001-7230-5784, Dartmouth Institute for Health Policy and Clinical Practice, corresponding author), Andrea M Austin (0000-0002-4404-6065, Dartmouth Institute for Health Policy and Clinical Practice, corresponding author), Qianfei Wang (0000-0002-6297-3954, Dartmouth Institute for Health Policy and Clinical Practice, corresponding author), Diane Feskanich (0000-0001-8180-8690, Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA), Jefferson Bynum (0000-0003-1481-4462, Dartmouth Institute for Health Policy and Clinical Practice, corresponding author), Julie P W Bynum (Dartmouth Institute for Health Policy and Clinical Practice), Francine Grodstein (0000-0002-2699-7051, Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA), Anna N A Tosteson (0000-0001-7718-8943, Dartmouth Institute for Health Policy and Clinical Practice, corresponding author)
Year2018
Volume56
Issue12
Pagese83-e89
Publication date2018-12-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000875
PMID29334524
OpenAlexW2783239163
LanguageEN
Citations received3
References cited14

BACKGROUND: In an effort to overcome quality and cost constraints inherent in population-based research, diverse data sources are increasingly being combined. In this paper, we describe the performance of a Medicare claims-based incident cancer identification algorithm in comparison with observational cohort data from the Nurses' Health Study (NHS). METHODS: NHS-Medicare linked participants' claims data were analyzed using 4 versions of a cancer identification algorithm across 3 cancer sites (breast, colorectal, and lung). The algorithms evaluated included an update of the original Setoguchi algorithm, and 3 other versions that differed in the data used for prevalent cancer exclusions. RESULTS: The algorithm that yielded the highest positive predictive value (PPV) (0.52-0.82) and κ statistic (0.62-0.87) in identifying incident cancer cases utilized both Medicare claims and observational cohort data (NHS) to remove prevalent cases. The algorithm that only used NHS data to inform the removal of prevalent cancer cases performed nearly equivalently in statistical performance (PPV, 0.50-0.79; κ, 0.61-0.85), whereas the version that used only claims to inform the removal of prevalent cancer cases performed substantially worse (PPV, 0.42-0.60; κ, 0.54-0.70), in comparison with the dual data source-informed algorithm. CONCLUSIONS: Our findings suggest claims-based algorithms identify incident cancer with variable reliability when measured against an observational cohort study reference standard. Self-reported baseline information available in cohort studies is more effective in removing prevalent cancer cases than are claims data algorithms. Use of claims-based algorithms should be tailored to the research question at hand and the nature of available observational cohort data

Algorithm · Breast cancer · Cancer · Cohort · Cohort study · Data mining · Environmental health · Observational study · Population · Advanced Causal Inference Techniques · Computer Science · Global Cancer Incidence and Screening · Internal Medicine · Medicine · Survey Methodology and Nonresponse