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Understanding Limitations While Debunking Myths in Public Health Research Utilizing Insurance Clms Data
Lisa M. Hess, PhD, Katherine B. Winfree, PhD, Catherine E. Muehlenbein, MPH, Yajun E. Zhu, MS, MBA, Ana B. Oton, MD, Nicole Princic, MS, and Himani Aggarwal, PhD
In recent years, there has been a growing recognition of the potential benefits that can be realized from incorporating health insurance clms data into public health research efforts. In Debunking myths about health insurance clms data for public health research and practice, Cozad et al.1 highlight the advantages of using these data sources, emphasizing their widespread avlability compared to electronic medical records EMRs. While we enthusiastically orse this notion and encourage increased utilization, it is crucial that researchers are aware of specific limitations inherent in insurance clms data.
One prevling myth is that relevant outcome measures might not be fully captured within clms databases. This assumption holds only when considering the scope of questions being posed; indeed, many studies have successfully employed clms data to address inquiries regarding diagnosis codes and procedures with relative ease. However, there are cases where the desired outcomessuch as disease severity or progressionare not systematically recorded or may vary in reporting across different datasets. The application of proxy variables has been a common approach in these scenarios; however, their utility is contingent upon adequate validation and adherence to rigorous quality standards.
The second myth relates to clms data being considered unreliable for research purposes. While they can provide robust evidence for clinical decision-making and drug development, researchers must be aware that the merging of clms data with other datasets such as EMRs or US Census Bureau records presents significant challenges. When only a portion of the population can be successfully linked between these sources, both sample size considerations and potential impacts on generalizability become critical factors to address. This limitation underscores the need for thorough evaluation of missing variables within any given data set before proceeding with research design.
A third myth is that insurance clms lack insight into real-world clinical practicesa perceived weakness in their utility. We argue that this view misunderstands a fundamental strength of these data sources: they offer unparalleled insights into routine healthcare operations and patient management. However, it is essential to adhere to accepted research standards to mitigate risks associated with flawed study designespecially when using clms data for public health investigations.
In , we support Cozad et al.'s1 argument for increased use of administrative clms data in efforts med at enhancing the nation's health outcomes. Nonetheless, it is imperative that this orsement does not overshadow the need to acknowledge limitations and mntn strict adherence to best practices when designing research studies utilizing insurance clms databases.
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CONFLICTS OF INTEREST
L.M.H., K.B.W., C.E.M., Y.E.Z., A.B.O., and H.A. are employees of Eli Lilly and Company and hold stocks in the company. N.P. is an employee of IBM Watson Health. The authors have no other conflicts of interest to disclose.
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REFERENCES
Grimes DA, Schulz KF. Bias and causal associations in observational research. Lancet 2002;3599302:248-257.
Motheral B, Brooks J, Clark MA et al. A checklist for retrospective database studiesreport of the ISPOR Task Force on Retrospective Databases. Value Health 2003;62:90-97.
Garbe E, Siebert U, Johnson ML. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources. Value Health 2009;128:1053-1061.
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