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

ABSTRACT: 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 ? 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; ?, 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; ?, 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.

SUBMITTER: Bronson MR

PROVIDER: S-EPMC6043405 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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

Bronson Mackenzie R MR Kapadia Nirav S NS Austin Andrea M AM Wang Qianfei Q Feskanich Diane D Bynum Julie P W JPW Grodstein Francine F Tosteson Anna N A ANA

Medical care 20181201 12

<h4>Background</h4>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).<h4>Methods</h4>NHS-Medicare linked participants' claims data were analyzed using 4 versions of a cancer identification algorithm across 3 can ...[more]

PMID: 29334524

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Project description:BackgroundLinking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]).ObjectiveThis study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions.MethodsA Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay.ResultsThe whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization.ConclusionsThis study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.

Dataset Information

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

Publications

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

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