Cathy J Bradley, Rifei Liang, Jagar Jasem, Richard C Lindrooth, Lindsay M Sabik, Marcelo C Perraillon
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引用次数: 0
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.
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.