Evidence map›Paper›PMID 33074744›Full record

ArticleJCO clinical cancer informatics2020

Consolidation of Cancer Registry and Administrative Claims Data on Cancer Diagnosis and Treatment in the US Military Health System.

Yvonne L Eaglehouse, Amie B Park, Matthew W Georg, Derek W Brown, Jie Lin, Stephanie Shao, Julie A Bytnar, Craig D Shriver, Kangmin Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yvonne L EaglehouseMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.ORCID 0000-0002-9077-4688
Amie B ParkMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.
Matthew W GeorgMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.
Derek W BrownMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.ORCID 0000-0001-8393-1713
Jie LinMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.
Stephanie ShaoMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.
Julie A BytnarMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.ORCID 0000-0002-7813-8232
Craig D ShriverMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.ORCID 0000-0001-8993-5811
Kangmin ZhuMurtha Cancer Center/Research Program, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLinked cancer registry and medical claims data have increased the capacity for cancer research. However, few efforts have described methods to select information between data sources, which may affect data use. We developed a systematic process to evaluate and consolidate cancer diagnosis and treatment information between the linked Department of Defense Central Cancer Registry (CCR) and Military Health System Data Repository (MDR) administrative claims database, called Military Cancer Epidemiology Data System (MilCanEpi).

methodsMilCanEpi contains information on cancer diagnosis and treatment of patients receiving care from 1998 to 2014. We used an iterative process guided by knowledge of data features, current literature, and logical comparisons between the CCR and MDR data to evaluate and consolidate cancer diagnosis and treatment received (yes or no) and their dates. We applied the processes to breast cancer data as an example. Agreement between diagnosis and treatment dates in the two data sources was evaluated using Cohen's κ with 95% CIs.

resultsIn MilCanEpi, we identified 15,965 patients with a breast cancer diagnosis and 15,145 patients who underwent breast cancer surgery; 97.9% and 84.1% of patients had records in both CCR and MDR for diagnosis and surgery, respectively. Exact agreement was 13.7% for diagnosis dates (Cohen's κ = 0.14; 95% CI, 0.13 to 0.14) and 68.9% for surgery dates (Cohen's κ = 0.69; 95% CI, 0.68 to 0.70) between the two data sources. After applying systematic processes, 98.1% of patients with a breast cancer diagnosis and 99.7% of patients with surgery had information selected for analytic data sets.

conclusionThe developed processes resulted in high consolidation rates of breast cancer data in MilCanEpi and may serve as a data selection template for other tumor sites and linked data sources.

Indexed as

Breast NeoplasmsMilitary Health ServicesDatabases, FactualFemaleHumansInformation Storage and RetrievalRegistries

Identifiers

PMID33074744
PMCPMC7608597

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.