The CRWDi infrastructure was developed by consolidation and linkage of multiple real-world data sources of patients in the United States, and is now available to the national research community (https://seer.cancer.gov/data-software/crwdi). Collectively, CRWDi constitutes a non-random sample of real-world data informed by COVID-19 status, on a substantive population of immunocompromised patients, with a comparator general population. The fact that the general inclusion criteria were for deidentified patients who had COVID-19 related data, including negative as well as positive laboratory test results for COVID-19 (virologic and/or serologic), means that research studies will be informed by patients who did experience SARS-CoV-2 infection, in comparison to those that did not.

Having been established, this data resource also can be updated. Further, by modifying the inclusion criteria, the CRWDi approach to dataset creation also can be replicated relatively quickly in the event of a different health crisis or to address other important clinical or public health questions. Importantly, the use of PPRL for the consolidation of the multiple data sources represents a relatively new but effective method to bring together data components that only together can answer questions not possible using a single data source, however large (30).

While real-world data cannot replace clinical trials, the ability to leverage real-world datasets allows investigators and governmental agencies to focus on important subpopulations who are unlikely to be eligible for clinical trials. CRWDi may also be useful to validate observations obtained from other large, national consortia, particularly those based solely on electronic health records. A strong advantage of CRWDi is identification through administrative claims of longitudinal data on patients who have received care from multiple health systems, and who have obtained and filled pharmacy prescriptions in the ambulatory setting. Indeed, information on ambulatory pharmaceutical treatment for both COVID-19 and co-existent conditions such as cancer or autoimmune disease provides a powerful enhancement for understanding the impact of COVID-19 in these higher risk patients (32). Although the claims-based foundation of CRWDi omits patients who do not have health insurance, this dataset is otherwise drawn from the entire U.S. population, and so should yield generalizable findings.

A limitation of the NCI SEER Registry is both that the data are through December 31, 2021 only, and that SEER data may be incomplete with regards to subsequent courses of therapy, disease progression, or recurrence (17). Linkage of SEER data to the claims data of HealthVerity provides access to extensive information about the longitudinal clinical course of cancer patients through December 31, 2023, thus substantively extending the chronologic reach of the SEER Registry or the SEER-Medicare database (17).

The integration of novel data sources such as the SEER Registry data and Northwell data supplements the existing HVM claims and commercial laboratory data in important ways. The SEER data represent carefully adjudicated information on incident cancer cases that are followed longitudinally, information that is not available through administrative claims data. In turn, by providing a large sample of serologic test results (n=376,259), the Northwell Health data provide critical opportunity for validation of the potential value of COVID-19 serologic testing. The fact that Northwell Health data identified an additional n=295,891 patients whose COVID-19 related laboratory data could be linked to the HVM demonstrates the potential importance of including data from health systems in development of a harmonized national data infrastructure for study of public health emergencies.

There are certain limitations of CRWDi. First, we required that a patient have continuous benefits enrollment for at least 180 days during the study period of December 1, 2018 to December 31, 2023 to assure comprehensive capture of information during the covered period. There is the potential of selection bias against patients who contracted COVID-19 and succumbed in less than 180 days. However, preliminary analysis of the CRWDi dataset shows that most patients with a history of cancer from the SEER Registry were enrolled in benefit plans for much longer periods (median enrollment was of duration >1500 days, data not shown). This suggests that benefits enrollment for immunocompromised patients is more likely to have predated an index date for SARS-CoV-2 infection, and hence accelerated severe courses of COVID-19 would be captured. Second, while one measure of diagnosis of infection with SARS-CoV-2 included presence of a positive NAAT, with the advent of home antigen testing a substantial portion of patients with SARS-CoV-2 infection who did not seek medical care may be missed, raising the possibility of bias towards null outcomes (33). However, since the scientific focus for CRWDi is to assess risk for severe disease and/or post-COVID-19 sequelae, it is likely that the CWRDi will have a high capture rate for this important subset of the SARS-CoV-2 infected population. Third, CRWDi relies on medical coding, e.g., ICD-10, NPC, and CPT, and reporting of cancer diagnoses (SEER). The limitations of administrative coding data are well-documented, with regards to potential diagnostic or procedural coding inaccuracies (34–36). The capture of health resource utilization data as well as diagnostic and treatment coding data should help overcome this potential limitation, by informing researchers on the health care needs of patients separate from the diagnoses those patients might be assigned.

There are other infrastructures that represent important data sources to study SARS-CoV-2 (37–42); these sources represent complimentary but distinct data consolidation efforts. Such data resources also are subject to the potential limitations of accuracy and reliability in administrative coding of health care encounters. The electronic health record-based consortia and the federal Sentinel program represent real-world data that provide a different perspective on the COVID-19 pandemic than the CRWDi, and thus these multiple systems are complementary to one another.