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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
A Payer–Provider Partnership for Integrated Care of Patients Receiving Dialysis
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.
ABSTRACT

Objectives: To evaluate the impact of enrollment in a consumer-directed health plan (CDHP) on out-of-pocket (OOP) spending and on the financial burden associated with healthcare utilization.

Study Design: Using commercial claims data from 2011 through 2013, we estimated difference-in-differences models that compared changes in outcomes for individuals who switched to CDHPs (CDHP group) with outcome changes for individuals who remained in traditional plans (traditional plan group).

Methods: We estimated the impact of CDHP enrollment on OOP spending at the point of care and on having high financial burden, defined as whether an enrollee spent 3% or more of household income on OOP spending. Additionally, we assessed these outcomes for 2 subgroups: those with lower household income and those with chronic conditions.

Results: Within the first year of CDHP enrollment, CDHP enrollees experienced a mean marginal increase in OOP spending of $285 (41% increase; 95% CI, $271-$299; <.001) relative to traditional plan enrollees. The lower-income and chronic conditions subgroups experienced mean marginal increases in OOP costs of $306 (44% increase; 95% CI, $257-$353; <.001) and $387 (56% increase; 95% CI, $339-$435; <.001), respectively. The probability of an enrollee having excessive financial burden increased by 4.3 percentage points (95% CI, 4.0-4.6; <.001) for the full CDHP sample. These effects were about 3 times larger for the lower-income subgroup (12.3 percentage points; 95% CI, 10.7-13.8; <.001) and 2 times larger for the chronic conditions subgroup (8.0 percentage points; 95% CI, 6.9-9.1; <.001).

Conclusions: CDHP enrollment led to a significant increase in financial burden associated with healthcare utilization, especially for those with lower incomes and those with chronic conditions.

Am J Manag Care. 2018;24(4):e115-e121
Takeaway Points

Consumer-directed health plan (CDHP) enrollment increases the financial burden associated with healthcare utilization, especially for those with lower incomes, those with chronic conditions, and those at the higher end of the healthcare spending distribution. 
  • Due to the unpredictable nature of healthcare utilization, it is unclear in advance who will end up with high healthcare utilization and significant financial burden in a given year. 
  • More effort is needed to make individuals aware of the potentially significant out-of-pocket spending and financial burden that could be incurred after enrollment in a CDHP.
Consumer-directed health plans (CDHPs) are high-deductible health plans (HDHPs) coupled with a health savings account (HSA) or a health reimbursement arrangement (HRA). The proportion of covered workers with employer-sponsored insurance enrolled in a CDHP increased from 4% in 2006 to 29% in 2016.1In 2016, 56% of covered workers were employed by a firm that offered a CDHP and, among the 41% of covered workers who were offered only 1 type of plan, nearly one-third were offered only a CDHP.1

CDHPs are designed to make patients more cost-conscious and to encourage value-based decision making. Although previous work has consistently demonstrated that CDHPs reduce healthcare spending and utilization,2-9 they may also increase financial burden when patients utilize healthcare. Previous research has shown that CDHP enrollees are more likely to have difficulty accessing necessary healthcare and to incur high medical bills and medical debt,10,11 particularly among lower-income individuals and those with chronic conditions.10-14

However, many existing studies are based on findings from cross-sectional surveys and thus might be subject to potential selection bias. We therefore investigated the impact of CDHP enrollment on the financial burden of healthcare utilization at the point of service, using longitudinal private insurance claims data from January 1, 2011, to December 31, 2013. We estimated the effects of CDHP enrollment on out-of-pocket (OOP) costs both at the mean and across the distribution of healthcare spending. Additionally, we analyzed the effect of CDHP enrollment on the probability of an enrollee having excessive financial burden from OOP spending. Finally, given evidence that lower-income individuals and the chronically ill may be more likely to forgo or delay care due to cost, coupled with the fact that the RAND Health Insurance Experiment suggests that high cost sharing may lead to impaired health outcomes among the poor and sick,15-18 we examined the effects of CDHP enrollment on financial burden for these vulnerable populations. 

METHODS

Data and Sample

We used claims data from the OptumInsight (a subsidiary of UnitedHealth Group) Clinformatics Data Mart from 2011 to 2013 to analyze a 25% random sample of the insurer’s commercially insured subscribers and dependents who were continuously enrolled over the full 3-year period in both employer-sponsored and nongroup plans. These claims data also include measures of socioeconomic status, including categorical household income, predicted by a demographic-based analytical model using Census block group–level data where the unit population size (~600-3000) is smaller than that of a Census tract (~1200-8000) or a zip code tabulation area.19 The data also include measures of education level, predicted by the median level of education attained by individuals 25 years or older at the Census block level, and race/ethnicity code, derived from a combination of sources including public records, self-reported surveys, and a proprietary ethnic code algorithm.

We included 2 groups of enrollees. The CDHP group included enrollees who were in a traditional plan in 2011 and in a CDHP in 2012 and 2013 (switch date: January 1, 2012) and enrollees who were in a traditional plan in 2011 and 2012 and in a CDHP in 2013 (switch date: January 1, 2013); switching was split relatively evenly across the 2 years in the study sample (eAppendix Table [eAppendix available at ajmc.com]). The traditional plan control group included enrollees who were in a traditional plan throughout all 3 years. We excluded enrollees who were 65 years or older (5.7% of the sample) and enrollees with negative OOP costs (0.009% of the sample). The lower-income subgroup included enrollees with an estimated annual household income of less than $40,000 (the lowest category defined in the data). The chronic conditions subgroup was defined as enrollees with at least 1 chronic condition in the baseline year, defined by the Charlson Comorbidity Index (CCI).20 The data were deidentified, and this study was approved by the University of Southern California University Park Institutional Review Board.

Outcomes

We analyzed OOP spending and a binary indicator of excessive financial burden, defined by OOP spending being greater than or equal to 3% of household income, based on prior research.12 We also used alternative thresholds (5% and 10%) to examine the sensitivity of our findings to this definition.21 In all cases, OOP spending refers only to spending incurred at the point of care and represents the sum of co-payments, deductibles, and coinsurance paid by an enrollee for all healthcare services utilized in the given year. Because estimated household income was a categorical variable in our data, we calculated the excessive financial burden indicator using the midpoint of each income interval, capped at $100,000 (the highest category defined in the data). 

Statistical Analysis

We used the χtest and the test to compare baseline characteristics between the CDHP group and the traditional plan group. We then performed descriptive analyses of the trends in mean OOP spending and the percentage of enrollees having excessive financial burden before and after CDHP enrollment. We used difference-in-differences (DID) regression analysis to compare changes in the outcomes for individuals who switched to CDHPs (CDHP group) with those for individuals who remained in traditional plans (traditional plan group). Compared with a standard DID model in which the participants in the treatment group usually experience a 1-time shift, we analyzed enrollees who switched to a CDHP at 2 different time points to enable estimation of both short-term (1-year) and medium-term (2-year) effects of CDHP enrollment. In all models, the analysis was at the enrollee-year level, and the primary independent variables were the indicators for the first and second year (where applicable) of CDHP enrollment, adjusted for group fixed effects, year fixed effects, age, gender, race, education, Census division, and CCI score.

For each population, we first estimated the impact of CDHP enrollment on mean OOP spending. Because OOP spending was highly skewed, we used a generalized linear model (GLM) with log link function and gamma distribution. We calculated the average marginal effects (incremental effects) of first- and second-year CDHP enrollment. Standard errors were clustered at the enrollee level. We also investigated the impact of CDHP enrollment on the distribution of differences in OOP spending (relative to traditional plan enrollees) using the linear quantile DID (QDID) model,22 which outputs the treatment effects of CDHP enrollment at each decile of OOP spending. We used the QDID regression coefficients to predict traditional plan enrollees’ adjusted OOP spending distribution in 2013 and the traditional plan group’s counterfactual OOP spending distribution (ie, what the OOP spending distribution of the traditional plan group would have been had they enrolled in a CDHP). Based on the adjusted and counterfactual OOP cost distribution, we examined the change in the percentage of enrollees who would have very high OOP spending (eg, higher than $2000) due to CDHP enrollment. Finally, we analyzed the impact of CDHP enrollment on the indicator of whether an enrollee had excessive financial burden (OOP spending to household income ratio ≥3%, 5%, or 10%) in a given year using GLM with logit link function and Bernoulli distribution. Standard errors were clustered at the enrollee level. Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc; Cary, North Carolina) and Stata version 14 (StataCorp; College Station, Texas). We used a significance level of P ≤.05 and all statistical tests were 2-sided.



 
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