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The American Journal of Managed Care April 2018
Delivering on the Value Proposition of Precision Medicine: The View From Healthcare Payers
Jane Null Kogan, PhD; Philip Empey, PharmD, PhD; Justin Kanter, MA; Donna J. Keyser, PhD, MBA; and William H. Shrank, MD, MSHS
Care Coordination for Children With Special Needs in Medicaid: Lessons From Medicare
Kate A. Stewart, PhD, MS; Katharine W.V. Bradley, PhD, MBA; Joseph S. Zickafoose, MD, MS; Rachel Hildrich, BS; Henry T. Ireys, PhD; and Randall S. Brown, PhD
Cost Sharing and Branded Antidepressant Initiation Among Patients Treated With Generics
Jason D. Buxbaum, MHSA; Michael E. Chernew, PhD; Machaon Bonafede, PhD; Anna Vlahiotis, MA; Deborah Walter, MPA; Lisa Mucha, PhD; and A. Mark Fendrick, MD
The Well-Being of Long-Term Cancer Survivors
Jeffrey Sullivan, MS; Julia Thornton Snider, PhD; Emma van Eijndhoven, MS, MA; Tony Okoro, PharmD, MPH; Katharine Batt, MD, MSc; and Thomas DeLeire, PhD
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Justin Kindy, FSA, MAAA; David Roer, MD; Robert Wanovich, PharmD; and Stephen McMurray, MD
Currently Reading
Financial Burden of Healthcare Utilization in Consumer-Directed Health Plans
Xinke Zhang, PhD; Erin Trish, PhD; and Neeraj Sood, PhD
Physician and Patient Tools to Improve Chronic Kidney Disease Care
Thomas D. Sequist, MD, MPH; Alison M. Holliday, MPH; E. John Orav, PhD; David W. Bates, MD, MSc; and Bradley M. Denker, MD
Limited Distribution Networks Stifle Competition in the Generic and Biosimilar Drug Industries
Laura Karas, MD, MPH; Kenneth M. Shermock, PharmD, PhD; Celia Proctor, PharmD, MBA; Mariana Socal, MD, PhD; and Gerard F. Anderson, PhD
Provider and Patient Burdens of Obtaining Oral Anticancer Medications
Daniel M. Geynisman, MD; Caitlin R. Meeker, MPH; Jamie L. Doyle, MPH; Elizabeth A. Handorf, PhD; Marijo Bilusic, MD, PhD; Elizabeth R. Plimack, MD, MS; and Yu-Ning Wong, MD, MSCE

Financial Burden of Healthcare Utilization in Consumer-Directed Health Plans

Xinke Zhang, PhD; Erin Trish, PhD; and Neeraj Sood, PhD
Enrollment in a consumer-directed health plan increases the financial burden associated with healthcare utilization, especially for those with lower incomes and with chronic conditions.

We estimated the impact of CDHP enrollment on financial burden due to cost sharing incurred at the point of service when utilizing care. We found that CDHP enrollment led to a significant increase in OOP spending and that this higher financial burden was particularly pronounced for the lower-income and chronic conditions subgroups. For example, more than half of the lower-income subgroup and more than one-third of the chronic conditions subgroup faced excessive financial burden from OOP spending after enrollment in a CDHP. Moreover, we found that CDHP enrollment increased the probability of having very high OOP spending. 


Our sample was subject to potential selection concerns because we were unable to evaluate whether CDHP enrollees actively chose a CDHP among a menu of plan options or whether it was the only option made available to them by their employer, although the fact that the level and trend of the key outcome variables were similar between the CDHP and traditional plan groups in the baseline period reduces these concerns (eAppendix Figures 1 and 2). Additionally, the household income data were based on a prediction model and thus may be subject to measurement error, although the predictions were based on very local geography. We were also unable to link dependents to subscribers, so we could not assess financial burden due to OOP spending at the household level; however, because the income levels represent household (rather than individual) income whereas we assessed individual-level OOP spending, our estimates may thus represent a lower bound of financial burden on OOP spending pooled across all household members. Finally, our data did not include additional detail, such as plan premiums or whether and how much the employer contributed to the individual’s HSA or HRA, so we could not directly evaluate the impact of CDHP enrollment on overall spending.


Our findings, and in particular, the variation in the impact of CDHP enrollment on OOP spending across the distribution, should be considered in the broader context of overall healthcare spending, including premiums and potential HSA/HRA contributions. Nationally representative data on employer-sponsored health benefits indicate that, in 2013, the average annual worker contribution to premiums for single coverage was $1058 for CDHPs with an HRA, $726 for CDHPs with an HSA, and $1027 for non-CDHP plans. This suggests that enrollees in CDHPs with an HSA saved $301 ($1027 minus $726) in employee-paid premium contributions compared with those enrolled in non-CDHPs, on average. Additionally, 34% of covered workers in CDHPs with an HSA worked for an employer that did not make an HSA contribution but, among those that did, the average annual HSA contribution for a single employee was $950.24

At the lower end of the distribution, we found very modest increases in OOP spending at the point of care among CDHP enrollees compared with traditional plan enrollees. Thus, it is likely that these individuals may actually be financially better off by enrolling in CDHPs, as the savings in employee-paid premium contributions and/or the benefits of employer contributions to a tax-advantaged savings account likely more than offset the increased OOP spending at the point of care. However, at the high end of the distribution, the increased OOP spending would still heavily outweigh the reduced spending on employee contributions to premiums, particularly if the individual was among the one-third of covered workers who did not receive an HSA contribution from their employer. Moreover, even if those individuals at the lower end of the actual spending distribution may have lower total expenditures on premiums plus OOP spending at the point of care in a given year, due to the unpredictable nature of catastrophic health events, they still face the potential risk of having very large OOP spending under a high deductible, which may be particularly problematic for lower-income individuals with limited assets.25

The potential consequences of CDHP enrollees experiencing high financial burden are significant. Previous work suggests that high financial burden may cause CDHP enrollees to delay or skip needed healthcare.10,11,26 Additionally, individuals who face financial burden often have to change their employment or lifestyle, or make other sacrifices, to make ends meet.26 We do not know the extent to which individuals considered potential downstream OOP costs when enrolling in CDHPs. However, prior research suggests that many individuals make poor health plan choices and frequently choose worse plans when alternative choices that have both lower financial risk of OOP spending and lower premiums are available.27,28 A possible explanation for these poor choices is that many individuals do not understand the basics of benefit design, such as the meaning of deductibles and co-payments.29,30 Moreover, this problem of poor health insurance literacy is more acute among the low-income population,30 suggesting that these individuals may not understand the potential financial burden associated with enrollment in CDHPs. Although prior work suggests that providing information on OOP costs can improve health plan choice,28,31 more work is needed to design effective interventions for improving plan choice. Additionally, further research on the impact of financial burden associated with CDHP enrollment on the use of appropriate care, unmet medical need, and other health outcomes is warranted.

Author Affiliations: School of Pharmacy (XZ), Price School of Public Policy (ET, NS), and Schaeffer Center for Health Policy and Economics (XZ, ET, NS), University of Southern California, Los Angeles, CA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

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

Address Correspondence to: Neeraj Sood, PhD, Price School of Public Policy, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-3333. Email:

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