Evidence map›Paper›PMID 35923286›Full record

ArticleCancer informatics2022

Cancer Treatment Data in Central Cancer Registries: When Are Supplemental Data Needed?

Cathy J Bradley, Rifei Liang, Jagar Jasem, Richard C Lindrooth, Lindsay M Sabik, Marcelo C Perraillon

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
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

9 citing papers in PubMed, 13 citations in OpenAlex.

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  7. Much work to do about measuring work.Journal of the National Cancer Institute · 2024
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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

6 authors at 3 institutions in 1 country.

Cathy J BradleyUniversity of Colorado Cancer Center, Aurora, CO, USA.
Rifei LiangUniversity of Colorado Cancer Center, Aurora, CO, USA.
Jagar JasemUniversity of Colorado Cancer Center, Aurora, CO, USA.
Richard C LindroothColorado School of Public Health, Aurora, CO, USA.
Lindsay M SabikUniversity of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.
Marcelo C PerraillonUniversity of Colorado Cancer Center, Aurora, CO, USA.
Colorado School of Public Health · USUniversity of Colorado Denver · USUniversity of Pittsburgh · US

Funding

University of Colorado Cancer Center Support Grant - Lung Cancer Patient-Derived Xenografts with Autologous Human Immune SystemsP30CA046934 · NCI · UNIVERSITY OF COLORADO DENVER · PI James V Degregori · 1988 to 2026
$117.0M
Addressing Urban Rural Disparities in Cancer: The Case for Registry ExpansionR01CA229551 · NCI · UNIVERSITY OF COLORADO DENVER · PI BRADLEY, CATHY J, PERRAILLON, MARCELO COCA · 2019 to 2022
$1.6M
NCI NIH HHS P30 CA046934NCI NIH HHS R01 CA229551
6 · The paper itself

Abstract

Background: We evaluated treatment concordance between the Colorado All Payer Claims Database (APCD) and the Colorado Central Cancer Registry (CCCR) to explore whether APCDs can augment registry data. We compare treatment concordance for breast cancer, an extensively studied site with an inpatient reporting source and select leukemias that are often diagnosed outpatient. Methods: We analyzed concordance by cancer type and treatment, patient demographics, reporting source, and health insurance, calculating the sensitivity, specificity, positive predictive values (PPV) and Kappa statistics. We estimated an adjusted logistic regression model to assess whether the APCD statistically significantly reports additional cancer-directed treatments. Results: Among women with breast cancer, 14% had chemotherapy treatments that were absent from the CCCR. Missing treatments were more common among women younger than age 50 (15%) and patients aged 75 and older (19%), rural residents (17%), and when the reporting source was outpatient (22%). Similar and more pronounced patterns for people with leukemia were observed. Concordance for oral treatments was lower for each cancer. Sensitivity and PPVs were high, with moderate Kappa statistics. The APCD was 5.3 percentage points less likely to identify additional treatments for breast cancer patients and 10 percentage points more likely to identify additional treatments when the reporting source was an outpatient facility. Conclusion: A robust data infrastructure is needed to investigate research questions that require population-level analyses, particularly for questions seeking to reduce health inequity and comparisons across payers, including Medicare Advantage and fee-for-service. APCD data are a step toward creating an infrastructure for cancer, particularly for patients who reside in rural areas and/or receive care from outpatient centers.

Indexed as

all payer claims dataCancer registriesdisparitiesMedicare Advantage

Identifiers

PMID35923286
PMCPMC9340909
OpenAlexW4288787423

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.