Birthday Reflections: NCDB as a Tool for Fighting Structural Racism

The National Cancer Database (NCDB) is a hospital-based cancer registry, collecting standardized data on diagnostic, staging, treatment (including time to treatment), and outcomes for more than 70 percent of newly diagnosed cancer patients in the United States, and includes patients residing in all states. The NCDB has been a valuable resource for identifying disparities throughout the cancer care continuum.^1-3 Research using the NCDB data can also offer important insights into the impact of policy changes, such as the Affordable Care Act, on access to cancer care.⁴ Additionally, the sheer size of NCDB allows for investigation of health disparities and the impact of policy changes among patients diagnosed with rare cancers,⁵ and in specific subpopulations of patients frequently neglected in cancer research due to small sample sizes, such as transgender, HIV-positive, or young adult cancer patients.^6-8

More recently, efforts to understand and better address persistent racial and ethnic disparities in care and outcomes have included greater focus on social determinants of health, which are the conditions in the environments where people are born, live, learn, work, play, worship, and age.  The medical community is advancing its understanding of the role structural racism, a key social determinant of health, plays in access to health care. Structural racism is not simply the result of private prejudices held by individuals, but is also produced and reproduced by laws, rules, and practices, sanctioned and even implemented by various levels of government, and embedded in the economic system as well as in cultural and societal norms.⁹ The NCDB is a unique data source for investigating the role of structural racism in disparities in access to cancer care and outcomes

The strengths of NCDB for studying disparities become even more apparent when one considers the inherent limitations of other data sources commonly used for research investigating health disparities. For example, relying on cancer registry data linked with administrative health insurance claims data, such as Medicare, to estimate factors contributing to racial disparities is problematic. By definition, all patients in these datasets have health insurance coverage. This lack of heterogeneity can lead to over-estimation of the contribution of individual-level factors and under-estimation of the contribution of system-level factors (i.e. health insurance coverage) to racial disparities. These biases help perpetrate erroneous perceptions that biologic factors and individual behaviors account for racial differences in health outcomes and obfuscates the role of structural racism in determining health outcomes.¹⁰

In contrast, NCDB collects standardized cancer treatment information for all analytic cases,¹¹ regardless of age, health insurance coverage, or social economic status. The large number of patients, patient population heterogeneity, and the depth of information collected (patient, tumor, treatment, time to treatment, comorbidities, facility, health insurance coverage, and area-level socioeconomic characteristics) of the NCDB population makes it a  valuable resource for identifying potentially modifiable factors contributing to racial disparities in cancer care and outcomes.¹²

NCDB is a hospital-based, not a population-based, registry. However, the NCDB population is strikingly similar to that of population-based registries,¹³ underscoring the representativeness of NCDB and strengthening the generalizability of research findings using NCDB. While a critical consideration with NCDB data is the extent to which data are missing at random,¹⁴ it is important to remember that some population-based registries do not include several states with large non-Hispanic Black and Hispanic populations.^15,16 Data on these patient population is therefore missing not at random in population-based registries that exclude these states, and can lead to misestimation of disparities and erroneous conclusion regarding factors that contribute to observed disparities. Furthermore, as a hospital-based registry, research using NCDB is uniquely positioned to identify hospital-level levers for addressing structural racism.

While information on patient-level social-determinants of health is valuable,^17,18 it can be challenging to collect.^19,20 In contrast, facility-level information (e.g., social services provided, psychosocial distress screening performed, number of patients referred to services) is already routinely collected as part of the CoC accreditation process.²¹ Previous studies using NCDB data have relied on proxies to identify safety-net facilities.²² However, collection of safety-net or Federally Qualified Health Center status, among other facility characteristics, directly from the facilities would be more accurate, allow facilities to identify efforts that are working, and facilitate use of these health system-level variables in research investigating determinants of disparities in cancer care nationally.²³

Another role of the NCDB is to serve as a quality control tool for the accredited facilities, allowing each facility to assess progress in compliance with quality measures and compare their performance with other hospitals. Value-based payment models used by many insurers routinely use quality measures in determining the value of care that patients receive.  However, clinical outcomes depend not only on the care that patients receive, but also on the underlying risk profile of patients served by the facility.²⁴ Hospitals serving patients with higher risk of adverse outcomes may be penalized because of the characteristics of the patients they serve rather than the quality of care they offer, and relying on risk adjustment (based on patients’ clinical and demographic characteristics) to fairly judge facilities’ performance is challenging.^24-30 Furthermore, risk adjustment in value-based payment models might minimize financial penalties for facilities serving high-risk patient populations, but it doesn’t help ensure equitable access to high-quality care regardless of social characteristics in a system such as the CoC accreditation. In contrast, using quality metrics to identify racial disparities in access to cancer care (within and among institutions), linking health equity measures to accreditation programs, and incentivizing hospitals to engage in equity improvement efforts, can help mitigate the impact of structural racism in healthcare.³¹

For example, the CoC could recommend that researchers using NCDB data adhere to appropriate standards for conducting scholarly research and reporting on racial disparities.^10,32 There are also several data collection initiatives that could impact our ability to investigate and tackle structural racism and health disparities, including collecting patient identifying information (PII), patient-reported outcomes (including experiences of discrimination),³³ recurrence, chemotherapy agents,³⁴ physician workforce characteristics,^35-41 clinical trial participation,^42,43 use of patient navigators,^44,45 and pre-diagnostic screening information, for example.

Collection of PII is one initiative with great potential, as it would improve data usefulness not only for investigating cancer disparities, but also for investigating topics relevant to all cancer patients (by allowing multiple primaries from the same patient to be identified in NCDB, for example). Using PII to consolidate patient information (including race/ethnicity, clinical characteristics, treatment, etc.) reported from different facilities would also decrease the registrars’ data collection burden and improve NCDB data accuracy and completeness. Additionally, consolidating data reported from different facilities would allow for a better understanding of different patient’s access to resources and their trajectory through cancer care. Furthermore, collecting PII would allow NCDB data to be linked with external data sources, such as the National Death Index (NDI), facilitating follow-up data collection, or to the All-Payer Claims Database, facilitating collection of treatment and comorbidities data. Similarly, linking NCBD data with the North American Association of Central Cancer Registries (NAACCR) Hispanic and Asian/Pacific Islander Identification Algorithm (NHAPIIA) would improve racial/ethnic data accuracy in NCDB.¹³

Instead of requiring registrars to extract an ever-growing number of data elements manually as the number and complexity of cancer treatment options expands, an infrastructure that facilitates Health Insurance Portability and Accountability Act (HIPAA)-compliant data linkage, extraction, and reporting should be prioritized.⁴⁶ Ideally, data linkage should be performed with a unique identifier⁴⁷ between healthcare systems that are interoperable.^48,49 Indeed, the American College of Surgeons recently joined efforts for developing an Unique Patient Identifier (UPI),⁵⁰ for adding cancer data elements to the United States Core Data for Interoperability (USCDI).⁵¹

Notably, the American College of Surgeons (ACS) has recognized its responsibility to address the issue of structural racism, and in 2020, the ACS appointed a task force of senior leaders to provide recommendations on how to address issues related to racial inequalities and structural racism. The task force’s report included, among other recommendations, using ACS Quality Programs to identify barriers with access to cancer care.⁵² Similarly, the National Comprehensive Cancer Network, The American Cancer Society Cancer Action Network, and the National Minority Quality Forum recently published recommendations to reduce racial disparities in access to guideline-concordant cancer care, including collecting information on social-determinants of health.⁵³ In the centenary anniversary of the creation of the CoC, we are well-positioned to go beyond declarative advocacy and undertake specific, data-driven actions to address structural racism and disparities in cancer care.⁵⁴

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