Leveraging Healthcare Access Data to Understand Cancer Disparities
Several main categories of big data resources may be useful for studying and improving breast cancer care. The first type is administrative claims data. Generally, these data are obtained for research from large insurance providers (such as the Centers for Medicare and Medicaid Services [CMS], for Medicare and Medicaid data), linked data resources from a state or geographic region (such as the North Carolina Integrated Cancer Information and Surveillance System [ICISS], for claims from multiple payers in the state of North Carolina), or pools of commercial insurance payers who have agreed to aggregate de-identified data (such as the MarketScan database of Truven Health Analytics, an IBM Watson Health company). Strengths of administrative claims data include the detailed and temporal record of treatments provided to patients; and the representation of diverse patients across geography, age, socioeconomic status, and other categories. Claims data follow patients from one site of care to another; these records remain intact despite changes in healthcare providers over time, but there may be disruptions in access if a patient changes insurance providers. Claims generally contain information about costs of care, making them particularly valuable in cost and value analyses. Claims data also generally contain information on both cancer patients and otherwise similar patients without a cancer diagnosis, facilitating the design of case-control studies. Limitations of claims data for cancer disparities research are often the result of a lack of clinical detail regarding cancer stage and other clinical characteristics, as well as outcomes of treatment such as recurrence and survival.
Some limitations of claims data can be obviated by linking with other data sources. A wide variety of other data types have been linked to administrative claims. Data linkages that can enrich understanding of the patient’s disease include cancer registry data, as in the SEER-Medicare and ICISS datasets, patient-reported data such as the Medicare Health Outcomes Survey of beneficiaries, and laboratory data. Linkages of claims and registry data to other sources such as census data and the National Death Index have also enhanced the ability to characterize patients’ socioeconomic situations and their survival after cancer treatment. However, data sources are not available to provide additional information on all desired variables regarding treatment, comorbidities, or other variables likely to affect outcome, and even when linked there may not be adequate patient overlap between the clinical and administrative data. In these cases, researchers can sometimes apply claims-based algorithms to define important exposures, such as the receipt of a certain treatment or the burden of other illnesses, or to define outcomes of interest, such as treatment toxicities, cancer recurrence, or cancer-related death. Depending on the complexity of the algorithm, the availability of a dataset with clinical annotation to validate the algorithm, and the specificity of the billing codes used in the clinical situation of interest, such algorithms can be of greater or lesser value for research. Breast cancer recurrence has been especially challenging to identify accurately in claims, although published and validated algorithms do exist,[28,29] while progression of disease in the metastatic setting is virtually impossible to ascertain. An additional challenge in disparities research is that racial and ethnic minorities are disproportionately uninsured and overrepresented in the databases of public insurers, so that these populations in any one particular claims database may be small and/or not representative of the experience of other patients.
Along with the growing use of EHRs in clinical practice, there is increased leveraging of the vast amounts of data collected within these systems for use in cancer research, including disparities research. Advantages of these data sources include the rich clinical detail not available in claims, the representation of patients across the spectrum of insurance types and age ranges, and the potential to provide accurate information about cancer outcomes. The original research using pooled EHR data was performed in integrated health systems such as Kaiser Permanente and the Group Health Cooperative. Efforts are now underway to aggregate EHR data from oncology practices and hospitals, with a specific focus on providing data for cancer research and quality improvement.
Leveraging Biological Data to Understand Cancer Disparities
A substantial contribution to racial disparities in mortality is differential access to healthcare, which can be directly tracked using the previously discussed resources. However, racial disparities can also arise from differences in tumor biology, and this factor requires distinct types of big data to elucidate. Differences in exposure history over a lifetime, genetic background, and social factors may lead to differences in breast tumor biology or clinical subtype. For example, as discussed, black women have higher relative frequency of all aggressive breast cancer subtypes (luminal B, HER2-enriched, basal-like) and lower relative frequency of the most treatable, indolent luminal A breast cancers.[6,9,10] In the decade since these relative frequency differences were first identified, there has been an evolution in the technology that we use to identify specific tumor subtypes. In early reports of racial disparities by subtype,[6,8] immunohistochemistry data were collected for five markers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, cytokeratin 5/6, and epidermal growth factor receptor), and these immunohistochemistry surrogates were used to approximate genomic subtypes. However, the use of only three to five markers to approximate subtypes that were originally identified across thousands of gene expression features is an error-prone approach that several recent studies have demonstrated can result in substantial misclassification.[30,31] In research settings, initiatives such as The Cancer Genome Atlas project have begun to identify biological subtypes using a broad range of genomic markers, including DNA-sequencing, RNA-sequencing, DNA-methylation, microRNA-sequencing, and protein arrays. Even histologic images of breast tumors can be mined to extract many visual patterns and features, and new prognostic algorithms have been developed through multidimensional analysis of data from such images. In research settings, the number of features that can be used to classify tumors has increased by many orders of magnitude. Different data types can also be brought together in complex ways to identify both commonalities and differences between tumors from different sites.
Some of the most exciting recent advances, and groundbreaking changes in our understanding of cancer, have come from leveraging parts of complex genetic datasets that were previously considered uninformative. For example, previous research has focused on gene-specific “driver” mutational events that lead to specific breast cancer subtypes, and a relatively small number of driver mutations (~30 gene-specific mutations) have been identified, with few racial differences between them. For example, The Cancer Genome Atlas project has elegantly demonstrated that most gene-specific mutations (eg, p53, PIK3CA, etc) are strongly associated with certain clinical subtypes of breast cancer, and that after adjusting for molecular subtype, few differences are detected in mutational frequency by race. More racial differences have been identified when considering gene-independent characteristics, such as the degree of intratumoral heterogeneity, which may be higher in black women. Furthermore, while the number of genes that are frequently mutated across breast cancers is relatively small, the number of mutations harbored in a single cancer genome is manyfold higher, ranging from hundreds to thousands. The biological causes and epidemiology of these mutations are almost entirely unknown, but many of these mutations are simply passenger mutations. Passenger mutations, in contrast to driver mutations, do not have a positive or negative effect on tumor development and are not selected for during development. However, recent research using data from The Cancer Genome Atlas on 30 different cancer types has demonstrated that the patterns of mutations, inclusive of both driver and passenger events, show that there are recurrent mutational signatures that are commonly observed.[35,36] Many of these mutational signatures have been linked to exposure history, and some may eventually be found to be prognostic. Studies have not yet been performed to compare mutational signatures by race. Thus, we have just begun to understand the many ways in which the somatic genomics of black women and white women may progress along different paths to disease. Moving from gene-specific to higher-order relationships among features is challenging and will require continuing methodological advances. Progress in these areas is essential to capturing the full impact of complex genomic data.
Continuing research is generating complex information on the leading edge of data science, and much of this research has already begun to have an impact on clinical practice. Genomic tests such as the Oncotype DX assay, MammaPrint, and the Prosigna Breast Cancer Prognostic Gene Signature Assay have become widely available in clinical settings. Among breast cancer patients with HR-positive, HER2-negative disease, there are a subset with higher risk of recurrence scores, both by Oncotype DX and the Prosigna assays. Wider availability of molecular data would improve our understanding of the heterogeneity of outcomes within a clinically defined group. However, as previously described, barriers to healthcare access have prevented equitable use of such genomic assays in black women. It is likely that advances in genomic science will continue to drive novel, precision medicine approaches for addressing disparities. Leveraging this science will require continued development of data analysis and integration methods, and methods for combining the genomic data with data on healthcare utilization. Data integration has begun to bring together clinical data and genomic data, but extending the technologies employed to include a wider range of data types, over time and across research settings, remains a research frontier.
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