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The American Journal of Managed Care August 2018
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Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers
Karen Mulligan, PhD; Jeffrey Sullivan, MS; Lara Yoon, MPH; Jacki Chou, MPP, MPL; and Karen Van Nuys, PhD
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Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers

Karen Mulligan, PhD; Jeffrey Sullivan, MS; Lara Yoon, MPH; Jacki Chou, MPP, MPL; and Karen Van Nuys, PhD
An optimized hepatitis C virus screening and linkage-to-care process reduces the number of patients lost to follow-up and improves linkage to care for Medicare, Medicaid, and commercially insured patients.
Limitations

We note several limitations. Our parameter values come from the literature and were not available for all insurance types in many cases. Some of our parameters may not be generalizable because they are derived from small samples or high-risk subpopulations. In cases where required parameter values were unavailable, we relied on assumptions, detailed in the eAppendix, to populate the model.

Although the consolidated scenario demonstrates the value of streamlining the SLTC process, it represents a hypothetical process. Novel real-world screening models, such as Project ECHO, attempt to achieve similar efficiency gains through telemedicine, but they have not been broadly adopted.31 Additionally, the decision to initiate treatment is a dynamic one (ie, patients who are not initially recommended for treatment may receive a treatment recommendation later). Because we model “no treatment recommended” as an absorbing state, we do not capture the dynamics of the treatment recommendation decision and therefore underestimate the number of patients who ultimately initiate treatment, as well as the costs.

Although our model captures key screening steps and barriers related to obtaining treatment, it relies, like all models, on simplifications and abstractions that may not generalize. For example, we do not consider variability within insurers; our classification of a single broad “commercial” stratification does not allow for the effect of plan-specific features, such as narrow networks, on the SLTC process. We also do not consider the site where patients are screened or the composition of patients receiving screening, both of which may impact screening outcomes. Our assumption that fibrosis staging can be reflexed could result in some patients’ fibrosis scores being misclassified because blood tests are not sensitive enough to rule out substantial fibrosis.20,32,33

Finally, we do not explicitly model capacity constraints, but we model wait times between stages. Explicitly including capacity constraints would further affect patient wait times between stages, particularly in consolidated, which assumes that the entire SLTC process occurs at a single site.

Future research should focus on identifying opportunities to improve the STLC process for patients across screening sites and insurance providers, as well as collecting more granular real-world data for the SLTC process. Other real-world features should be considered, such as the decision to enter screening, dynamic treatment recommendations, and capacity constraints. Finally, it will be useful to understand the relative importance of other mechanisms for improving SLTC process efficiency, such as patient navigation, decreased wait times between appointments, and conducting all HCV screening and additional care at 1 location.

CONCLUSIONS

Substantial advances in treatment have improved the outlook for patients with HCV, but continuing efforts are needed to increase the number of patients who complete the SLTC process. Appropriate care can increase the number of patients screened, evaluated, and treated for, and cured of, HCV. Initiatives to address the efficiency of the SLTC process should be tailored to reflect nuances in different insurance populations and access to resources. Our findings highlight the importance of removing inefficiencies in the early SLTC stages (eg, antibody and RNA testing). However, consolidating the early part of the SLTC process is not sufficient because patients also encounter barriers later, usually at the PA stage. Reducing the number of visits required to obtain treatment, as well as removing other barriers, will increase the number of patients who obtain treatment. 

Acknowledgments

The authors would like to thank Alisher Sanetullaev, Alison Silverstein, and Emma van Eijndhoven for their valuable research support.

Author Affiliations: Sol Price School of Public Policy and Schaeffer Center for Health Policy and Economics at the University of Southern California (KM); Precision Health Economics (JS, LY, JC), Los Angeles, CA; Value of Life Sciences Innovation Project, Schaeffer Center for Health Policy and Economics at the University of Southern California (KVN), Los Angeles, CA.

Source of Funding: Funding for this study was provided by Gilead Sciences, Inc, to Precision Health Economics.

Author Disclosures: At the time this research was conducted, Dr Mulligan, Mr Sullivan, Ms Yoon, and Ms Chou were employed by Precision Health Economics (PHE), which consults for pharmaceutical clients. Mr Sullivan owns options to purchase stock in PHE. Dr Van Nuys is a health economics consultant with clients in the pharmaceutical industry; she worked on this project as a consultant to PHE.

Authorship Information: Concept and design (KM, JS, LY, KVN); acquisition of data (KM, LY); analysis and interpretation of data (KM, JS, KVN); drafting of the manuscript (KM, LY, JC, KVN); critical revision of the manuscript for important intellectual content (KM, JS, JC, KVN); statistical analysis (JS); administrative, technical, or logistic support (JC); and supervision (KVN).

Address Correspondence to: Karen Mulligan, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Ste 500, Los Angeles, CA 90025. Email: karenmul@usc.edu.
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