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

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2022-07-30 eCollection Date: 2022-01-01 DOI:10.1177/11769351221112457
Cathy J Bradley, Rifei Liang, Jagar Jasem, Richard C Lindrooth, Lindsay M Sabik, Marcelo C Perraillon
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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.

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中央癌症登记处的癌症治疗数据:何时需要补充数据?
背景:我们评估了科罗拉多州所有支付者索赔数据库(APCD)与科罗拉多州中央癌症登记处(CCCR)之间的治疗一致性,以探讨 APCD 是否能增强登记处数据。我们比较了乳腺癌和白血病的治疗一致性,前者是一个被广泛研究的部位,有住院病人报告来源,而后者通常在门诊确诊:我们按癌症类型和治疗方法、患者人口统计学特征、报告来源和医疗保险分析了一致性,计算了灵敏度、特异性、阳性预测值 (PPV) 和 Kappa 统计量。我们估计了一个调整后的逻辑回归模型,以评估 APCD 是否在统计上显著报告了额外的癌症定向治疗:结果:在乳腺癌女性患者中,有 14% 的化疗疗程在 CCCR 中缺失。在 50 岁以下女性(15%)、75 岁及以上患者(19%)、农村居民(17%)以及报告来源为门诊患者(22%)中,遗漏治疗的情况更为常见。在白血病患者中也观察到类似且更明显的模式。每种癌症的口服治疗一致性都较低。灵敏度和 PPV 均较高,Kappa 统计量适中。当报告来源为门诊机构时,APCD 识别乳腺癌患者额外治疗的可能性要低 5.3 个百分点,识别额外治疗的可能性要高 10 个百分点:调查需要人群水平分析的研究问题需要一个强大的数据基础设施,特别是对于寻求减少医疗不公平的问题以及不同支付者(包括医疗保险优势和付费服务)之间的比较。APCD 数据是创建癌症基础设施的一个步骤,尤其是对于居住在农村地区和/或接受门诊中心治疗的患者而言。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
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.
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