Dennis Ross-Degnan, ScD

 To determine the impact of high-deductible health plans (HDHPs) on health care use among individuals with bipolar disorder.

 Interrupted time series with propensity score—matched control group design, using

a national health insurer’s claims data set with medical, pharmacy, and enrollment data.

 The intervention group was composed of 2862 members with bipolar disorder who were enrolled for 1 year in a low-deductible (≤$500) plan and then 1 year in an HDHP (≥$1000) after an employer-mandated switch. HDHP members were propensity score matched 1:3 to contemporaneous controls in low-deductible plans. The main outcomes included out-of-pocket spending per health care service, mental health—related outpatient visits (subclassified as visits to nonpsychiatrist mental health providers and to psychiatrists), emergency department (ED) visits, and hospitalizations.

 Mean pre— to post–index date out-of-pocket spending per visit on all mental health office visits, nonpsychiatrist mental health provider visits, and psychiatrist visits increased by 21.9% (95% CI, 15.1%-28.6%), 33.8% (95% CI, 2.0%-65.5%), and 17.8% (95% CI, 12.2%-23.4%), respectively, among HDHP vs control members. The HDHP group experienced a –4.6% (95% CI, –11.7% to 2.5%) pre- to post change in mental health outpatient visits relative to controls, a –10.9% (95% CI, –20.6% to –1.3%) reduction in nonpsychiatrist mental health provider visits, and unchanged psychiatrist visits. ED visits and hospitalizations were also unchanged.

After a mandated switch to HDHPs, members with bipolar disorder experienced an 11% decline in visits to nonpsychiatrist mental health providers but unchanged psychiatrist visits, ED visits, and hospitalizations. HDHPs do not appear to have a “blunt instrument” effect on health care use in bipolar disorder; rather, patients might make trade-offs to preserve important care.

High-deductible health plan (HDHP) members with bipolar disorder experienced a moderate decrease in nonpsychiatrist mental health outpatient visits, but rates of psychiatrist visits, medication use, emergency department visits, and hospitalizations did not change.

HDHP members with bipolar disorder might elect to pay more out of pocket to maintain psychiatrist care and associated medication use but not nonpsychiatrist mental health provider care.

HDHPs do not appear to have a “blunt instrument” effect on health care use in bipolar disorder; rather, patients might make trade-offs to preserve important care.

Policy makers, employers, and health plans could use these findings to construct highly efficient value-based or tailored health insurance designs that optimize health care use and spending; for example, plans might reduce out-of-pocket costs for nonpsychiatrist mental health provider visits to enhance use.

Bipolar disorder is a serious mental illness characterized by acute episodes of mania, hypomania, and depression. Bipolar spectrum disorders include subtypes bipolar I, bipolar II, and cyclothymia. These often have an early age of onset, high risk of suicide, and high rates of co-occurring psychiatric conditions such as substance use disorders.

 In the United States, the 12-month prevalence of bipolar disorder is 2.8% and the lifetime prevalence is 4.4%.

 Although persons with bipolar disorder can have asymptomatic periods, episodes of clinical instability and impairment are common.

 In addition to managing acute mood episodes, guideline-recommended care for bipolar disorder requires a chronic care model of evidence-based medications, ongoing follow-up, and often evidence-based psychotherapies during nonmanic phases of care, such as individual and group psychoeducation, cognitive behavior therapy, interpersonal social rhythm therapy, and family-focused therapy, to reduce depressive symptoms or prevent future manic or depressive episodes.

In an effort to control rising health care costs, payers and employers are increasingly adopting high-deductible health plans (HDHPs)

 that require high out-of-pocket payments.

 HDHP advocates believe that providing patients with information about the quality of medical services while exposing them to greater costs will create “activated healthcare consumers”

 who will seek higher-value health care, adopt healthy behaviors, and reduce future costs.

HDHPs have proliferated over the past decade. In 2018, 58% of covered workers had deductibles of $1000 or more and 26% had deductibles of $2000 or more.

 Based on findings going back to the RAND Health Insurance Experiment (HIE)

 of the 1970s and 1980s, high cost sharing is generally considered a “blunt instrument” that reduces all health care utilization among all types of patients. However, more recent studies have found that such reductions do not occur in all clinical situations

 HDHP effects on patients with serious mental illness including bipolar disorder are unknown.

We chose to study patients with bipolar disorder because it is a serious mental illness that is common enough among commercially insured persons to study robustly. Bipolar disorder is also a chronic condition and requires consistent access to medications and expensive specialist care. This population could be “tipped over the edge” by even small reductions in access given that suboptimal adherence to bipolar medications can result in debilitating episodes of mania or depression. We hypothesized that the higher out-of-pocket spending for specialist care under HDHPs would reduce mental health outpatient visits to both psychiatrists and nonpsychiatrist mental health providers and that reduced psychiatrist visits would reduce bipolar medication fills. The expected direction of emergency department (ED) visits and hospitalizations was uncertain.

We drew our commercially insured study population from a large national claims database with members enrolled between January 2003 and December 2012. Data included medical, pharmacy, and outpatient claims from members of a large national health plan.

We used an interrupted time series with propensity-matched comparison series research design.

 We included individuals with bipolar disorder who were enrolled in low-deductible plans during a baseline year. Some experienced an employer-mandated switch to HDHPs and were followed for a subsequent year. Others continued in low-deductible plans because their employers offered only these arrangements, and we followed these members as the control group. Thus, our study groups were not offered a choice of deductible level, minimizing self-selection.

Employer selection and study group identification.

 Our process for identifying study groups involved first identifying eligible employers and then eligible patients with bipolar disorder at those employers. In a given benefit year (the 12 months when an employer provides certain health insurance benefits), we classified employers as offering low- or high-deductible coverage based on offering exclusively annual deductibles of $0 to $500 or at least $1000, respectively (

]). We then defined the pool of potential HDHP group employers as those with at least 1 year of low-deductible coverage followed by at least 1 year of mandated HDHP coverage.

We defined the index date for employers that switched to HDHPs as the first day of the month when the switch occurred. We defined the index date for employers that did not switch plans as the first day of the month when their yearly account was renewed. Members had index dates as early as January 1, 2004, and as late as January 1, 2012. For each person, we defined the beginning of the baseline period as 12 months before the employer’s index date.

 Study cohort selection began with 53 million overall members up to age 64 years enrolled during 2000-2012 (

 we used a hierarchical algorithm and evidence of outpatient and inpatient bipolar disorder diagnoses captured in 

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes to classify patients as having bipolar I, bipolar II, or uncertain type (eAppendix section I[B]).

After applying this algorithm, the bipolar cohort included 333,780 members. After restricting to members who were continuously enrolled for at least 2 years in the previously defined employers, who were aged 12 to 64 years during the calendar year of the index date, who had a most recent bipolar diagnosis that occurred from 5 years to 4 months prior to the index date, and whose employers did not carve out mental health benefits, we had a prematch pool of 2950 HDHP members and 26,911 controls with bipolar disorder.

Primary measures included mental health outpatient visits, overall and subclassified into (1) visits to psychiatrists and (2) visits to nonpsychiatrist mental health providers (eg, psychologists, counselors, mental health social workers). We chose this categorization because among mental health specialists, psychiatrists typically prescribe medications for bipolar disorder, whereas nonpsychiatrist mental health specialists commonly provide psychotherapy. In secondary analyses, we also examined the remaining small subset of mental health visits to (3) non—mental health specialist clinicians such as primary care providers. To better understand changes in use of health care that was not specifically related to mental health, we also measured non–mental health–related office visits, overall ED visits, and overall hospitalizations. Non–mental health–related visits comprised all office visits (including hospital outpatient visits) not classified as mental health related using the approach described earlier. That is, they included visits to non–mental health specialists that had evaluation and management codes for office visits (except those that were mental health specific) and a primary or secondary diagnosis that was not related to mental health.

As a secondary measure, we assessed bipolar medication use to determine whether this key component of outpatient bipolar disorder therapy was affected. Our measure was the number of days per study year that the member had at least 1 medication that is recommended for the treatment of bipolar disorder (lithium, atypical antipsychotics, and select anticonvulsants) on hand. We also assessed out-of-pocket spending on these health services per member and per visit, event, or prescription. Details about measure construction are included in the eAppendix.

Using 2000 US Census block group data and validated methods,

 we classified members according to the income and education levels of their neighborhood (

 We applied the Johns Hopkins ACG algorithm, a validated measure that predicts mortality,

 to members’ baseline year to estimate general morbidity, including both mental and physical conditions. We matched study groups on morbidity level because patients with differing levels of morbidity will have differing cost exposure and contact with the health system. We first used geocoding to classify patients as being from white, black, Hispanic, or mixed neighborhoods and then, when available, overwrote a participant’s classification as Hispanic or Asian using the E-Tech (Ethnic Technologies) system,

 which analyzes full names and geographic locations of individuals. This validated approach of combining surname analysis and Census data has positive and negative predictive values of approximately 80% and 90%, respectively.

 Other covariates included age category (12-18, 19-29, and 30-64 years) and US region of residence (West, Midwest, South, Northeast).

Member-level clinical covariates included bipolar disorder type (type I, type II, or uncertain type) (eAppendix section I[B]), number of baseline outpatient psychiatrist and nonpsychiatrist mental health provider visits per quarter, substance use disorder diagnosis (

 codes 291.x-292.x, 303.xx-305.xx), number of mental health ED visits per quarter, number of baseline mental illness (bipolar disorder or depression) inpatient days per quarter, number of baseline 30-day equivalent bipolar medication fills per quarter, and number of months from first observed bipolar diagnosis to the index date.

Derived employer-level characteristics included employer size (1-49, 50-99, 100-249, 250-499, 500-999, or ≥1000 enrollees); index month/year; percentage of women; percentage of enrollees in each of 4 US regions and in income, education, age, and race/ethnicity categories; employer baseline cost level and trend derived from a standardized cost variable; and mean employer ACG score.

To further minimize potential selection effects, we used a 2-level (employer- and member- level) propensity score matching approach

 and estimated propensity scores predicting the likelihood of a mandated HDHP switch based on the employer-level covariates defined above. Within these quartiles, we performed the member-level match between contemporaneous HDHP and control group members in the match pool with index dates between January 1, 2004, and January 1, 2012 (matching details in eAppendix section I[D]). After propensity score matching, the final study sample included 2862 HDHP members with bipolar disorder and 7705 matched controls.

We compared baseline characteristics of our study groups using a standardized differences approach.

 To determine whether HDHP members might be dropping out of their employers’ health plans at different rates compared with control members, we compared characteristics of the groups that had at least 12, 13, 18, and 24 months of enrollment, counting from the beginning of the baseline period.

After analyzing interrupted time series data (eAppendix section I[E]) and finding that all primary outcome measures had parallel baseline trends (data not shown), we used generalized estimating equations in a difference-in-differences analytic framework to compare changes in outcomes among members in the year before and after the mandated HDHP switch vs controls. Regression models for outpatient visits and corresponding out-of-pocket spending used a negative binomial distribution and were adjusted for employer size, calendar year of index date, gender, age category, race, ethnicity, and neighborhood income level and education level. The term of interest was the 2-way interaction between an indicator of HDHP vs control group and an indicator of the year before vs after the index date. Using terms from the regression model, we then used marginal effects methods

to calculate mean adjusted baseline and follow-up outpatient visit rates and out-of-pocket spending, as well as absolute and relative changes.

 We used a similar approach to model ED visits, hospitalizations, and corresponding out-of-pocket spending, but we used a zero-inflated negative binomial distribution to account for patients with zero utilization and spending in these areas.

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After matching, all standardized differences between HDHP and control group characteristics were well below 0.2, indicating minimal differences (Table 1).

 The average age of HDHP and control members was approximately 37 years, and 62% in each group were women. Approximately 26% lived in low-income neighborhoods, 16% lived in low-education neighborhoods, 4% were Hispanic, and the mean (SD) ACG morbidity score was 2.1 (3.0). About half of members (HDHP, 45.4%; control, 48.0%) were enrolled through midsize employers with 100 to 999 enrollees. Differential dropout was minimal and thus should not bias our results (

In adjusted difference-in-differences analyses, out-of-pocket spending from before to after the index date for all medical services increased by 20.9% (95% CI, 15.7%-26-3%) among HDHP members relative to controls (

). HDHP members experienced a 21.9% (95% CI, 15.1%-28.6%) increase in mental health visit prices (spending per visit) in the follow-up year compared with baseline relative to control group members (

) but no detectable changes in overall mental health outpatient visits (relative change, —4.6% [95% CI, –11.7% to 2.5%]) (

). The HDHP group experienced a 13.1% (95% CI, 4.1%-22.2%) relative pre- to post increase in out-of-pocket spending per member for outpatient mental health visits (

Analyses by visit type revealed that, in the context of a 33.8% (95% CI, 4.3%-63.3%) relative increase in visit price, HDHP members reduced nonpsychiatrist mental health provider visits by 10.9% (95% CI, —20.6% to –1.3%) (Table 2 and Figure). In contrast, psychiatrist visits were unchanged (0.6% [95% CI, –9.3% to 10.6%]) despite a 17.8% (95% CI, 12.2%-23.4%) increase in visit price. Per-member out-of-pocket spending changes were 4.9% (95% CI, –9.0% to 18.9%) for nonpsychiatrist mental health provider visits and 15.5% (95% CI, 4.7%-26.4%) for psychiatrist visits (eAppendix Figure 3). Non–mental health provider visits for mental health reasons and corresponding out-of-pocket spending were unchanged (

). HDHP members experienced a —9.0% (95% CI, –23.5% to 5.5%) nonsignificant relative change in non–mental health visits (Table 2).

HDHP members experienced no detectable changes in the proportion of days with any bipolar medication on hand after the HDHP switch relative to controls (Table 2 and 

) despite an increase in 30-day fill price of 16.8% (95% CI, 10.3%-23.3%) at follow-up vs baseline. Similarly, ED visits and hospitalizations were unchanged (

), whereas per-event prices increased by 40.1% (95% CI, 20.5%-59.8%) and 38.7% (95% CI, 3.7%-73.8%), respectively.

After an employer-mandated transition to HDHPs, commercially insured members with bipolar disorder maintained stable overall mental health outpatient visit rates relative to similar patients who remained in low-deductible health plans. However, nonpsychiatrist mental health provider visits declined by 11% among HDHP members, whereas psychiatrist visits were unchanged despite increased prices per visit. We did not find statistically significant changes in use of bipolar medications, ED visits, or hospitalizations.

A potential interpretation is that HDHP members with bipolar disorder elected to pay more out of pocket to maintain psychiatrist care and associated medication use but not nonpsychiatrist mental health provider care. HDHP members with bipolar disorder might be trying to preserve visits that they needed for medication refills, changes, or dose adjustments. Or, given that visits to nonpsychiatrist mental health providers generally occur more frequently, patients might view reducing the frequency of such visits as a necessary trade-off to manage their out-of-pocket expenses. It is difficult to interpret the lack of ED visit changes, in part because visits could simultaneously decrease because of HDHP financial barriers and increase because of inappropriately deferred care, leaving overall rates unchanged. In addition, HDHP members might not be able to reduce this key source of care given that research has demonstrated that individuals with bipolar disorder use the ED to obtain urgent mental health care.

 Previous research has not addressed effects of HDHPs on outpatient care among patients with mental health conditions. Earlier studies examined the broad impact of cost sharing on mental health care utilization. For example, the RAND HIE of the 1970s and 1980s found that higher cost sharing led to lower mental health care utilization compared with the demand for general medical services.

 In contrast, a more recent study found that patients’ price sensitivity for mental health and general medical care was comparable.

 Similar to our results, that study found significant variation in price sensitivity within types of mental health care (eg, visits, medications). However, these older studies did not examine populations with serious mental illness and were unable to distinguish cost-sharing effects on patient visits to different provider types. We are also not aware of research regarding the association between HDHP enrollment and changes in nonpsychiatrist mental health care visits, emergency care, and hospitalizations. In a context of reduced nonpsychiatrist mental health care visits, we found that rates of ED visits and hospitalizations did not change for HDHP members.

Substantially more is known about the impact of cost sharing on non—mental health care use and outcomes. The RAND HIE

 found that high out-of-pocket costs reduced almost all utilization to a similar degree. However, similar to our results among individuals with bipolar disorder, more recent studies have found that such reductions do not occur consistently in all clinical situations

 Recent research has generally found that when services are subject to the deductible, utilization decreases more among vulnerable members (eg, low-income, high-morbidity members) than less vulnerable members.

The RAND study also predicted that utilization decreases would not worsen health outcomes except among vulnerable members. Few modern studies have assessed health outcomes, but 2 detected evidence of increased adverse outcomes among low-income and high-morbidity members with diabetes.

Our study thus adds the novel insight that commercially insured patients with a serious mental illness might not experience cost sharing in HDHPs as a “blunt instrument” but rather make trade-offs to preserve certain types of mental health care. Policy makers, employers, and health plans could therefore use our findings to construct highly efficient value-based

 health insurance designs that optimize health care use and spending; for example, plans might reduce out-of-pocket costs for nonpsychiatrist mental health provider visits to enhance use while maintaining cost sharing for psychiatrist visits. Findings could also help clinicians recognize high-deductible insurance as a risk factor for patients with serious mental illness to reduce their use of certain mental health—related services.

Further research should investigate whether the reduced visits to nonpsychiatrist mental health providers that we detected cause adverse mental health or clinical events and should examine detailed medication adherence measures. Future studies should also address effects of HDHP transitions on patients with bipolar disorder who have lower incomes, higher morbidity, and higher levels of cost sharing under HDHPs with health savings accounts.

Our study has several limitations. We studied enrollees with employer-sponsored health insurance enrolled through a single national claims database. In addition, this study did not focus on the most vulnerable subset of members with bipolar disorder who might have public insurance or be uninsured. Nevertheless, given that HDHPs are almost exclusively a feature of commercial health insurance, our results should be generalizable to many individuals with bipolar disorder in employer-sponsored plans. Although we measured changes in ED visits and hospitalizations, these are imperfect health outcomes measures and our study was unable to assess important outcomes such as mania, depression,

 or suicide attempts. Our data did not allow us to classify members as the beneficiary, spouse, or dependent. Observational studies are subject to bias from unmeasured confounders. However, the controlled interrupted time series design that we used guards against most threats to validity.

 Finally, the patients included in our study were in different phases of their condition with regard to duration of recognized disease and disease control but unlikely to be imbalanced by study group.

HDHP members with bipolar disorder who faced substantial increases in cost sharing preserved psychiatrist visits while reducing nonpsychiatrist mental health provider visits. Further research should determine how such reduced care affects mental health and clinical outcomes such as suicide, mania, and depression.

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High-deductible health plan members with bipolar disorder experienced a reduction in nonpsychiatrist mental health provider visits but no changes in other utilization.

Effect of High-Deductible Insurance on Health Care Use in Bipolar Disorder

 New direct-acting antivirals (DAAs), introduced in late 2013, are effective for treating chronic hepatitis C virus (HCV) infection but may pose substantial financial burden on patients and health insurers. We examined HCV medication use and costs in a commercially insured population.

 We used claims data for 3091 individuals with HCV infection (2012-2015). Outcomes included HCV medication use, inflation-adjusted out-of-pocket (OOP) and health plan spending, and predictors of receiving new DAAs.

 Cumulatively, 9% of members with a diagnosis of HCV were treated with HCV medications in 2012 and this increased to 32% in 2015. Of 3091, 589 received new DAAs and 80% (n = 465) completed a 12-week treatment regimen. After new DAAs became available, average annual health plan spending on HCV medications increased from $2869 to $16,504 per HCV-diagnosed member (relative change, 475%; 95% CI, 352%-598%), and OOP spending increased from $41 to $94 (relative change, 131%; 95% CI, 15%-247%). Age (being aged 50-64 years [adjusted odds ratio (aOR), 2.13; 95% CI, 1.29-3.53] and being ≥65 years [aOR, 2.01; 95% CI, 1.14-3.55] compared with being <30 years) and having liver cirrhosis (aOR, 3.34; 95% CI, 2.64-4.21) were positively associated with receiving new DAAs, and a diagnosis of alcohol abuse (aOR, 0.70; 95% CI, 0.53-0.92) was negatively associated with receiving new DAAs.

The proportion of a commercially insured population with HCV infection who were treated with HCV medications doubled within 2 years following availability of new DAAs. Member OOP spending was kept low while the health plan bore 99% of the cost of HCV medications. During our 2-year follow-up, we did not observe financial benefits to the health plan of the cure of HCV infection by new DAAs.

New direct-acting antivirals (DAAs) are effective for treating chronic hepatitis C virus (HCV) infection but may pose substantial economic burden on patients and health insurers.

The proportion of a commercially insured population with HCV infection who were treated with HCV medications doubled within 2 years following availability of new DAAs.

In this health plan, member out-of-pocket spending was kept low while the health plan bore 99% of the cost of HCV medications. In our 2-year follow-up period, we did not observe financial benefits to the health plan of the cure of HCV infection by new DAAs.

The arrival of direct-acting antivirals (DAAs) started a new era of treatment for patients with chronic hepatitis C virus (HCV) infection.

 An estimated 80 million to 150 million individuals worldwide, including about 3 million to 4 million in the United States, have chronic HCV infection.

 If left untreated, chronic HCV infection can lead to cirrhosis, liver failure, and hepatocellular carcinoma

; it is the leading cause of liver transplantation and death from liver disease.

Sofosbuvir (Sovaldi), approved by the FDA in late 2013, was the first DAA indicated for treatment of HCV as part of an interferon-free regimen. In clinical trials, 12 weeks of treatment with sofosbuvir-based regimens achieved a sustained virologic response (“cure” or undetectable HCV RNA 12-24 weeks after therapy) rate of approximately 90% in patients with HCV. These regimens show much-improved tolerability and safety compared with poorly tolerated interferon-based therapy regimens that resulted in virologic response in only 20% to 30% of patients.

 Recurrent viremia (detectable HCV RNA at follow-up week 24 after achieving sustained virologic response) is extremely rare with sofosbuvir and ledipasvir/sofosbuvir (Harvoni) (12/3004 [0.4%] patients across 11 phase 3 trials

). Furthermore, adherence rates for DAAs are substantially higher than for interferon-based therapy (99.8% vs 36%-60%).

 Studies indicate that new DAAs are cost-effective in patients with HCV genotypes 1 through 5 at a threshold of $100,000 per quality-adjusted life-year.

 Since 2013, 8 other DAAs for HCV have been approved

The availability of DAAs helped to fuel the discourse about ever-rising spending on pharmaceuticals, driven by spending on specialty medications like DAAs.

 The published wholesale acquisition cost of sofosbuvir was $1000 per day in 2014 (or $84,000 for a 12-week course), with additional spending for required concomitant treatments. A single tablet of ledipasvir/sofosbuvir

was available at a published price of $1125 ($63,000, $94,500, and $189,000 for an 8-, 12-, and 24-week course, respectively).

 Although the high prices of specialty drugs are widely recognized, there were enormous concerns that demand for expensive DAAs for treatment of a prevalent condition might “bust” the health system budget.

 Concerns about budgetary impact and the need to contain pharmaceutical spending growth led payers to restrict initial access to DAAs to patients with higher liver fibrosis scores. However, even restricted subsidized access to DAAs could translate into substantial pharmaceutical spending for healthcare payers and/or out-of-pocket (OOP) spending for patients. In a commercially insured population with HCV infection, we examined the utilization of HCV medications, and health plan and member spending on them, both before and after the first availability of DAAs.

Harvard Pilgrim Health Care (HPHC) is a commercial health plan serving approximately 1.25 million members

 in Connecticut, Maine, Massachusetts, and New Hampshire; of these, about 700,000 members are fully insured with pharmacy data accessible to HPHC. Through prior authorization policies, HPHC, like many other health plans, initially restricted access to new DAAs to members with more advanced disease. Criteria for approval in 2014 and 2015 included age at least 18 years; a diagnosis of genotype 1/1b, 2, 3, 4, 5, or 6; liver disease staging within the past 3 years; evidence of stage 2 or greater hepatic fibrosis; being in the care of a gastroenterologist, infectious disease specialist, or hepatologist; and provider attestation that the patient was not currently participating in illicit substance abuse or alcohol abuse, or that the patient was receiving substance or alcohol abuse counseling services as an adjunct to HCV treatment (

). Once ledipasvir/sofosbuvir entered the market in 2014, it was the preferred medication in this health plan over other DAAs unless appropriate clinical justification was provided.

The study population consisted of fully insured HPHC members. We identified a rolling cohort of individuals who had at least 1 claim with an HCV diagnosis code (

International Classification of Diseases, Ninth Revision

] code 070.44, chronic hepatitis C with hepatic coma; or 070.54,

 chronic hepatitis C without hepatic coma)

 between January 1, 2012, and September 30, 2015 (end of the 

 coding era), and had evidence of pharmacy utilization (to ensure membership in a plan that included pharmacy coverage). We then grouped individuals by calendar year according to their periods of enrollment. Individuals were included in calendar year totals if they had at least 1 day of enrollment in that year; individuals could thus contribute to multiple years during their enrollment. We analyzed medical and pharmacy claims from January 1, 2012, through December 31, 2015.

We first calculated numbers of patients receiving at least 1 dispensing of old and/or new medications for HCV by calendar year and the total number of days supplied for each medication. Old HCV medications were boceprevir (Victrelis), telaprevir (Incivek), interferon and peginterferon, and ribavirin. New HCV medications included in our analysis were interferon-free regimens: daclatasvir dihydrochloride (Daklinza), ledipasvir/sofosbuvir (Harvoni), simeprevir sodium (Olysio), sofosbuvir (Sovaldi), ombitasvir/paritaprevir/ritonavir (Technivie), and ombitasvir/paritaprevir/ritonavir/dasabuvir sodium (Viekira Pak). Our analysis did not include elbasvir/grazoprevir (Zepatier), sofosbuvir/velpatasvir (Epclusa), sofosbuvir/velpatasvir/voxilaprevir (Vosevi), and glecaprevir/pibrentasvir (Mavyret) because they were approved by the FDA after the end of our study period.

We assessed the percentage market share (ie, percentage of pharmaceutical spending) for HCV medications overall and for individual products. Among the cohort of HCV-diagnosed individuals, we calculated OOP and health plan spending for medical and pharmacy (HCV and non-HCV medications) services per member per month and per year. OOP spending included co-payments, coinsurance, and deductible amounts. We also calculated OOP spending among users of both old and new HCV medications.

Demographic characteristics, derived from enrollment files, included age at time of first HCV diagnosis in the study period, gender, and health plan type (Medicare or commercial). Geographic measures of race/ethnicity, median household income, and education were derived from the 2015 American Community Survey 5-year estimates (2011-2015)

 based on an individual’s zip code. We classified members as residing in white, black, Hispanic, or other neighborhoods based on living in neighborhoods with 66% or more persons of the given race/ethnicity. We classified members from areas that did not fall into one of the race/ethnicity categories as residing in mixed race/ethnicity neighborhoods. We categorized median neighborhood household incomes in our study cohort in quartiles from lower to higher incomes. We classified members as residing in areas of high, middle, or low education level (ie, <15%, 15%-24.9%, or >25% of residents in the neighborhood areas with below-high-school education levels, respectively). Clinical characteristics (advanced liver disease, mental illness, and physical illness other than liver disease; see 

 for codes) were derived from claims data using the same diagnosis code—based definitions as previous research.

We used time series plots to display trends in market share of individual new HCV medications and per-member per-month OOP and health plan spending. Spending was adjusted to 2015 US dollars using the Consumer Price Index.

 We tested for trend in yearly rates using time series regression with a significance level of 

 <.05. We used pre—post analysis to examine changes in spending per HCV-diagnosed member before (2012-2013) and after (2014-2015) the availability of new DAAs. In our generalized estimating equations model,

 we used marginal effects methods to calculate mean adjusted baseline and follow-up spending as well as absolute and relative changes. Our models adjusted for annual changes in member-level covariates (age, sex, median household income, race/ethnicity [black/white], any advanced liver disease, HIV, any substance abuse, any mental illness, and any physical illness) and for enrollment length using offset in the negative binomial distribution. Finally, we used univariate and multivariate logistic regression models to identify demographic and clinical predictors of receiving new HCV medications. We fit a parsimonious multivariate model including variables that were statistically significant at 

 <.05 in univariate analyses and retaining key demographic and clinical variables regardless of statistical significance (sex, income, education, race/ethnicity, any mental illness, any physical illness). All analyses were carried out using SAS software version 9.3 (SAS Institute; Cary, North Carolina) and STATA 14 (StataCorp; College Station, Texas). The study was approved by the HPHC Institutional Review Board.

 describes study cohort characteristics, and 

 presents a cascading flowchart for construction of the cohorts. A total of 3091 HPHC members had at least 1 claim with an HCV diagnosis between January 1, 2012, and September 30, 2015, and evidence of pharmacy utilization during the study. The average age of the cohort was 52 years, and 66% were male. Almost one-fourth of the cohort lived in neighborhoods with median incomes below $51,835, and 78% lived in predominantly white and 78% in high-education neighborhoods.

Between 2012 and 2015, 1501 prior authorization applications for new HCV medications were submitted to the health plan. A total of 690 (46%) were approved. Lack of clinical supporting information or not meeting the fibrosis score criterion were the most common reasons for denial. A total of 164 appeals were received; of these, 53 applications (32%) were subsequently approved.

 summarizes prevalent use of both old and new HCV medications by year and medication. The proportion of members with treatment for chronic HCV infection increased from 9% in 2012 to 20% in 2015. Overall, 32% of the cohort received HCV medications during the study. A total of 589 members received new HCV medications. Of these, 79% (n = 465) members had sufficient supply to complete 12 weeks of new DAA therapy (another 41 individuals had medication availability that exceeded their enrollment period, but we were unable to determine duration of treatment).

 shows the market share of individual DAAs based on spending on HCV medications. Sofosbuvir (accounting for 68%) and ledipasvir/sofosbuvir (86%) were the most commonly used medications in 2014 and 2015, respectively. There was little use or no use of ombitasvir/paritaprevir/ritonavir, daclatasvir, ombitasvir/paritaprevir/ritonavir/dasabuvir, and simeprevir in this population. 

 shows the market share of individual DAAs based on numbers of patients treated.

 present average and median (with interquartile range) annual spending per HCV-diagnosed member, respectively. Following availability of new DAAs, average annual health plan spending on HCV medications increased from $2869 to $16,504 per HCV-diagnosed member (

). In adjusted pre—post analyses, the absolute change was $13,635 per member per year (95% CI, $11,708-$15,562) and the relative change was 475% (95% CI, 352%-598%). Medical spending for HCV-diagnosed members by the health plan remained stable over time. Overall, the health plan contributed more than 99% of the spending on HCV medications both before and after the availability of new DAAs. The 

 [A] illustrates monthly trajectories of health plan spending per HCV-diagnosed member.

 In this health plan, old HCV medications were generally on a lower tier of the pharmacy formulary (with an average co-payment of $27 per 30-day dispensing) whereas new DAAs were in a higher tier (with an average co-payment of $36 per 30-day dispensing). Some DAAs were “preferred” agents (eg, ledipasvir/sofosbuvir), thus coinsurance was not applied to these medications, keeping these medications more affordable than other specialty medications. Following availability of new DAAs, average yearly OOP spending on HCV medications increased from $41 to $94 per HCV-diagnosed member in the study population, with about 32% of members receiving HCV medications. In adjusted pre—post analyses, the absolute change was $53 (95% CI, $9-$97) per member per year and the relative change was 131% (95% CI, 15%-247%). Medical OOP spending did not change significantly following availability of new DAAs. The Figure [B] illustrates trends in monthly OOP spending trajectories per HCV-diagnosed member.

When we restricted analysis to only those receiving HCV medications, average annual OOP spending on HCV medications fell from $636 to $570 per user following availability of new DAAs, but this was not statistically significant (Table 3). In adjusted pre—post analyses, the absolute change was –$66 (95% CI, –$147 to $16) per user per year and the relative change was –10% (95% CI, –22% to 1.5%).

 presents multivariate regression results on predictors of receiving new DAAs. We found that age (being aged 50-64 years [adjusted odds ratio (aOR), 2.13; 95% CI, 1.29-3.53] and being ≥65 years [aOR, 2.01; 95% CI, 1.14-3.55] compared with being <30 years) and having liver cirrhosis (aOR, 3.34; 95% CI, 2.64-4.21) were positively associated with receiving new HCV medications. A diagnosis of alcohol abuse was negatively associated with receipt of new DAAs (aOR, 0.70; 95% CI, 0.53-0.92).

Our longitudinal study examined treatment of HCV infection in a commercially insured population before and after the initial availability of new and highly effective, but costly, DAAs. HCV medication uptake increased substantially following availability of new DAAs, which is not surprising given their high sustained virologic response rates and improved tolerability and safety compared with old HCV medications. In our study, almost 80% of individuals who initiated new DAAs may have completed a 12-week treatment regimen recommended for sustained virologic response, which is associated with lower risk of liver cancer and reductions in longer-term liver-related deaths or liver transplants.

 In our analysis, the identified predictors of receiving new DAAs largely reflected provider prescribing practice and insurance coverage policies for new DAAs at that time.

About half of the prior authorization applications were initially denied; failing to meet the fibrosis score requirement and missing clinical supporting information were the 2 main reasons for denial during the study period. When new DAAs first became available, severity of liver disease was a commonly used eligibility criterion for coverage with the intent to provide access to these expensive DAAs to the subgroup of patients with the highest immediate clinical need while managing resources.

 Following DAA treatment of the highest-risk patients, health plans modified reimbursement restrictions to enable broader access.

 The requirement of a high fibrosis score was later lifted by many payers,

 including the health plan in our study in 2016. In addition, the study health plan now allows prescribing of DAAs by nonspecialist physicians in consultation with a gastroenterologist, infectious disease specialist, or hepatologist to enhance patient access.

However, the study health plan still requires providers to attest that the patient is not participating in illicit substance abuse or alcohol abuse, or is receiving substance or alcohol abuse counseling services as an adjunct to HCV treatment (this requirement is to be lifted in 2020). We found that having a diagnosis of substance abuse (specifically, alcohol abuse) was negatively associated with receiving new DAAs, which is not surprising given the reimbursement requirements. Alcohol consumption has been associated with more rapid fibrosis progression.

 Moreover, the risk of reinfection among HCV-infected individuals who inject drugs could lower the efficient use of finite resources.

 This provider attestation requirement aims to ensure that patients are committed to limiting further liver damage and injectable drug use in an effort to prevent reinfection. Nevertheless, clinical guidelines

 suggest that recent or active injectable drug use is not a contraindication to DAAs, as adherence and efficacy rates are comparable between patients who do and do not use injectable drugs. The difference between prescribing restrictions and guideline recommendations possibly reflects, in part, the wedge between cost-effectiveness and affordability considerations.

 The availability of more DAAs has facilitated expanded access because of increased market competition, allowing commercial payers like HPHC

 seems to have decreased published prices,

 which is unusual before patent expiration (sofosbuvir’s patent will be the first to expire in 2028). The budgetary impact of expanded access should be considered in light of potential future cost savings related to treating patients early (eg, costs avoided for treating cirrhosis, liver failure, carcinoma, and liver transplantation).

Among this population of commercially insured patients with HCV, the advent of highly priced DAAs had a relatively small absolute impact on member OOP spending on HCV medications. Following availability of new DAAs, average annual OOP spending on HCV medications across members with this condition increased by about $53 per member; OOP spending on HCV medications did not change significantly among the subset of patients receiving HCV medications. However, average annual health plan spending on HCV medications per member with HCV increased by 475%, or $13,635, after the advent of new DAAs. The health plan contributed more than 99% of spending on HCV medications both before and after the availability of new DAAs. Given that the average membership duration is about 2 to 3 years in most commercial health plans,

 financial benefits following cure of HCV infection by new DAAs (eg, costs avoided for hospitalizations due to progressed disease or liver transplants) might not be realized during the enrollment span of most members receiving treatment. Indeed, we did not detect changes in medical spending by the health plan for the study population during the median 2.7-year follow-up.

 DAAs for HCV are only one class of high-priced specialty medications that pose increasing financial burden on patients, providers, payers, employers (because private payers are in many cases simply administering insurance services as decided by employers that self-insure), and society. In 2017, specialty medications accounted for 31% of spending on prescription medications under Medicare, 42% in state Medicaid programs, and 49% among commercial health plans.

 Specialty medications come to market at increasingly high prices, often costing more than $2000 per month per patient.

 Increased access to specialty medications will likely lead to higher premiums and/or deductibles, higher member cost sharing, or both, thus threatening affordability and equity in access to healthcare in general and potentially harming health.

 These are major concerns for the American public,

 and there is “a moral obligation to improve access [to DAAs and other highly effective, highly priced medications] by reforming approaches to drug pricing.”

 Payers and employers need to find ways to negotiate prices and streamline access for patients who can benefit most from effective innovations. Alternative payment approaches are being explored in the United States and worldwide. For example, the Louisiana Department of Health has approved a “subscription” or “Netflix” model to pay for HCV treatment for the state’s residents. This model involves a cross-payer coalition in the state: In exchange for subscription fees over a fixed number of years, the drug companies would provide access to HCV therapies and commit to patient and provider outreach efforts to enhance treatment rates.

 involves approximately AUD$1 billion (US$766 million) over 5 years in exchange for an unlimited volume of DAAs for HCV from drug companies. Early evidence suggests that such a model may result in lower per-patient prices, broader (or even universal) access to new drugs with certainty about the cost, and increases in market access for pharmaceutical companies with guaranteed revenue over the years of the agreement.

Our study has several limitations. First, claims data do not allow for studying outcomes, and a 4-year time window does not capture long-term clinical or financial impact for members or the payer. We did not have information on rebates to the payer or on manufacturer coupons to patients, so we may have overestimated their spending. For HCV medications, pharmaceutical manufacturer coupons are available to lower a member’s cost to $5 per prescription fill, up to a maximum of 25% of the catalog price of a 12-week regimen.

 However, the health plan’s net medication spending increased from 20% to 25% of total healthcare spending between 2011 and 2016, and ledipasvir/sofosbuvir was the health plan’s third most reimbursed product by value, accounting for $16 million net spending in 2016.

 OOP spending included co-payments, coinsurance, and deductible amounts, but we did not have information on premiums. We also did not have information on individuals who did not apply for prior authorization for DAA treatment nor on those who were denied approval. Given the data available at the time of the analysis, our study provides insights into the early experience of restricted access to DAAs; future research should examine the outcomes of expanded access to DAAs following removal of several reimbursement requirements and the decline of DAA drug prices. Lastly, an important economic component is the operation cost of prior authorization for these medicines, which is beyond the scope of this study. Prior authorization is commonly used to manage specialty medications. Prior authorization can be time-consuming, not only for providers who must complete paperwork but also for the health plan to review applications and adjudicate appeals. Prior authorization has been shown to be costly for all involved,

 and research is needed to estimate its costs given that more and more products, especially specialty medications, require prior authorization.

The proportion of a commercially insured population with HCV infection who were treated with HCV medications doubled within 2 years following availability of new DAAs. These medications can cure a prevalent, potentially fatal, chronic infectious disease, but they have a high price. Based on the experience of a regional commercial health plan, on average, patient OOP spending was kept low while the health plan covered 99% of spending on new HCV medications. In our 2-year follow-up period (similar to the average health plan enrollment spans of commercially insured members), we did not observe financial benefits of the cure of HCV infection by new DAAs.

The authors are grateful to Barbara Henry for her feedback on an early version of the manuscript.

 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute (CYL, DR-D, FZ, RL, CL, AW), Boston, MA; Harvard Pilgrim Health Care (MS), Wellesley, MA.

 This work was supported by a grant from Harvard Pilgrim Health Care Institute.

 Drs Lu, Ross-Degnan, Zhang, and Wagner; Mr LeCates; and Ms Lupton are employed by the Harvard Pilgrim Health Care Institute, which is affiliated with the health plan described in the study. Dr Sherman is a member of the Real Endpoints board of directors and is employed by Harvard Pilgrim Health Care.

 Concept and design (CYL, DR-D, MS, AW); acquisition of data (CYL); analysis and interpretation of data (CYL, DR-D, FZ, RL, MS, AW); drafting of the manuscript (CYL, RL, CL, AW); critical revision of the manuscript for important intellectual content (CYL, DR-D, FZ, RL, CL, MS, AW); statistical analysis (FZ, AW); provision of patients or study materials (CYL); obtaining funding (CYL, AW); administrative, technical, or logistic support (RL, CL); and supervision (CYL, AW).

 Christine Y. Lu, PhD, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Dr, Ste 401 East, Boston, MA 02215. Email: christine_lu@hphci.harvard.edu.

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Rates of hepatitis C virus (HCV) treatment in a commercially insured population doubled after availability of new direct-acting antivirals. Member out-of-pocket spending was kept low while the health plan bore 99% of spending on HCV medications.

Cost Burden of Hepatitis C Virus Treatment in Commercially Insured Patients

To determine the 2-year impact of high-deductible health plans (HDHPs) on highacuity, expensive medical care.

Retrospective pre-post, with propensity score—matched comparison group.

 We studied emergency department visits, hospitalizations, and related expenditures among 15,847 HMO members for 1 year before and up to 2 years after an employer-mandated switch to HDHPs, compared with 15,847 propensity-matched controls who remained in HMOs. Members were aged 1 to 64 years and insured between 2001 and 2008.

 Emergency department visits among HDHP members declined by 15.0% and 15.7% from baseline to the first and second follow-up years, respectively (95% confidence intervals −21.1% to −8.4% and −24.1% to −6.4%, respectively). Rates of nonemergent visits declined significantly in both years (−19.6% [−28.2% to−9.9%] and −18.1% [−29.8% to −4.4%], respectively), while intermediate-severity visits declined to a lesser degree (−13.4% [−23.0% to −2.5%] in the first and −10.9% [−24.4% to 5.1%] in the second follow-up year). Reductions in emergent visits were not detectable in either the first or second follow-up year (−9.7% [−26.9% to 11.5%] and −15.3% [−36.8% to 13.3%], respectively). Hospitalization rates decreased in the first follow-up year (−22.8% [−33.8% to −10.0%]), but hospitalization and cost reductions were not detectable by the second follow-up year (−11.8% [−27.9% to 7.9%] and 1.9% [−22.2% to 33.4%], respectively).

HDHP members experienced sustained reductions in emergency department visits over 2 years, but reductions in hospital utilization and costs were not apparent by the second year. Longer-term studies that assess deferred utilization and its effects are needed.

	This is the first study to examine the longer-term impact of high-deductible health plans on high-acuity, expensive medical care. Policy makers should consider closely monitoring enrollees for unintended consequences of cost sharing.

		 High-deductible health plans are expanding at unprecedented rates.

		 High-deductible health plan members who remained enrolled for up to 2 years had fewer, mostly nonemergent visits to the emergency department.

		 Initial large reductions in hospital utilization among high-deductible health plan members diminished by the second year.

 are increasingly turning to high-deductible health plans (HDHPs) to control rising health care costs. Compared with traditional employer-based plans, HDHPs have relatively low premiums but subject most services to annual deductibles of at least $1000.

; membership tripled between 2006 and 2010

 National health insurance reform could cause an “explosion” 

	The effects of HDHPs on healthcare utilization, costs, and health are controversial. Some suggest that HDHPs will reduce inappropriate medical services, control cost trends, and improve health outcomes.

 Others are concerned that HDHPs will decrease necessary healthcare, causing adverse health outcomes and increased longer-term costs.

	Prior studies including the RAND Health Insurance Experiment suggest that increased cost sharing reduces use of both appropriate and inappropriate health services, including hospitalizations,

 These effects may be associated with worse health outcomes.

In contrast, patients primarily reduce discretionary services when emergency department care is subject to cost sharing, and adverse outcomes have not been detected.

	A previous 1-year follow-up study of emergency department and hospital utilization after HMO members were switched into HDHPs found that HDHP members primarily reduced low-severity emergency department visits after an initial emergency department visit, and hospitalizations also declined.

 We hypothesized that care patterns among HDHP members in their second year of experience would be a better indicator curve” that occurs under HDHPs.

High-deductible health plan members had sustained reductions in emergency department visits over 2 years but initial reductions in hospital utilization and costs were not sustained.

Dennis Ross-Degnan, ScD

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