Does Formulary Benefit Design Affect Opioid Use and Misuse for - - PowerPoint PPT Presentation

Does Formulary Benefit Design Affect Opioid Use and Misuse for Disabled Medicare Beneficiaries?

Erin Taylor, Rosalie Malsberger, David Powell, Janice Blanchard, Andrew Mulcahy, and Rosalie Liccardo Pacula AcademyHealth ARM, June 2017

Background

• Misuse and abuse of prescription opioid analgesics remains an

important public health and policy problem

– Annual prescription opioid-related deaths increased more than 400% from 1999 to 2013 (Zhou et al. 2016) – Over one third of total annual economic burden ($78.5 billion) is due to health care cost and cost of treatment ($28.9 billion) (Florence et al. 2016)

• Literature has offered numerous suggested drivers, although role
• f insurance has received limited attention

– Clear evidence that insurance increases use of other Rx drugs (Duggan and Morton, 2011 and 2010) – Some recent evidence showing Part D insurance expansion played a role with prescription opioid misuse (Powell, Pacula, and Taylor, 2016)

Background

• If insurance is a driver, than insurance benefit design may be

effective tool in handling / reducing the problem, just as benefit design can deter excess utilization of other health care services

– Placement of opioids on higher cost sharing tiers may reduce utilization, by extension likelihood of misuse – Use of utilization management (UM) tools, such as prior authorization, may reduce likelihood of use and misuse

Purpose of this study: To assess whether Medicare Part D cost sharing and utilization management tools affect opioid use and misuse for disabled Medicare beneficiaries

Data Sources

• Medicare data

– FFS claims – Prescription Drug Event (PDE) data – Medicare Beneficiary Summary File (demographic information) – Part D benefit design data (cost sharing, UM tools, etc.)

• Population of interest: disabled FFS Medicare beneficiaries living

in CA or TX

– Focus on disabled beneficiaries due to higher opioid use for this population relative to general population (Meara et al., 2016; Morden et al., 2014)

• Timeframe: 2010 – 2012

Methods

• Exploit Part D benefit design variation across plans to test effects
• f cost sharing and UM tools on:

– Likelihood of opioid utilization (any opioids and opioid categories) – For opioid users, the likelihood of misuse

• Measure misuse using previously validated measures

– Trends and Risks of Opioid Use for Pain (TROUP) Score

• ranges from 0-16 (low to very high risk),
• combines:

– # of long- and/or short-acting days supplied over 6 months – # of pharmacies dispensing opioid prescription for patient (0-5+) – # of opioid prescribers (0-5+)

Methods, continued

• Calculate separate models by beneficiary Medicare/Medicaid

dual eligibility status

– Hypothesis: dual eligible beneficiaries not affected by cost sharing variation due to low, fixed copayments – Test effects of differential tier placement (e.g., different cost sharing)

• We estimate OLS models with fixed effects and clustered

standard errors at the plan level

– Control for patient- and plan-level factors associated with use, including age, gender, and plan premium

Defining “Mean Cost Sharing”

• Difficult to assess effects of cost sharing for an entire class of

drugs when benefit design is not linear

– Part D plans may offer deductible followed by tiered cost sharing

• Our approach: calculate an “average expected price”

– Calculate 30-day normalized cost sharing for opioids using plan data – Weight cost sharing for each plan by overall (across all plans) utilization of each opioid – If the deductible > $0:

• Assume patients pay full (average) price of drug if deductible > $0 and the

average price < deductible, or

• Assume patients pay the deductible + plan cost sharing if drug price is greater

than the deductible

Descriptive Statistics: Average Expected Price

$0.00 $50.00 $100.00 $150.00 $200.00 $250.00 $300.00

All Opioids Acet./Oxy. Hyd. Acet./Hyd. Bit. Acet./Cod. Phos. Hydrom. Hydrochl. Oxyc. Hydro. Fentanyl Morphine Sulf. Tram. Hydrochl.

Average age Expect cted ed OOP Price Opioid

• id Drug Categori
• ries

es All Benes (Users/Non-Users) Any Use TROUP > 0 TROUP = 0

Descriptive Statistics: Percent of Opioids with Quantity Limits

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%

All Opioids Acet./Oxy. Hyd. Acet./Hyd. Bit. Acet./Cod. Phos. Hydrom. Hydrochl. Oxyc. Hydro. Fentanyl Morphine Sulf. Tram. Hydrochl.

Percent nt with h Quantity ty Limits ts Applied ed Opioid

• id Drug Categori
• ries

es All Benes (Users/Non-Users) Any Use TROUP > 0 TROUP = 0

Descriptive Statistics (Beneficiary-Year)

Demogr

• graph

phics ics Full Samp mple (n (n = 1, 1,176, 6,112 12) Never er Dual (n = 2 239,970) 0) Always ys / Sometimes times Dual (n (n = 9 936,142) 2) Female 48.5% 46.3% 49.0% Age* 50.6 (10.5) 54.0 (8.9) 49.8 (10.7) Race White Black Hispanic Other/Unknown 51.5% 16.6% 26.1% 5.7% 61.8% 12.7% 20.6% 4.9% 48.9% 17.6% 27.5% 6.0% State of Residence: CA 60.5% 44.5% 64.6% Risk Score* 1.1 (0.6) 0.97 (0.6) 1.2 (0.7) * Mean with standard deviation in parentheses.

Descriptive Statistics (Beneficiary-Year), cont.

Full Samp mple (n (n = 1, 1,176, 6,112 12) Never er Dual (n = 239,970) 0) Always ys / Sometimes times Dual (n (n = 936,142) 2) Misuse Measures Any Opioid 47.7% 48.5% 47.5% TROUP Score (users)* 0.9 (1.8) 0.8 (1.6) 0.95 (1.8) Misuse Measure Components (Users Only) 90 Consecutive Days 6.6% 7.9% 6.3% # of Prescribers 2.2 (1.9) 1.95 (1.7) 2.2 (1.95) # of Pharmacies 1.6 (1.2) 1.5 (1.1) 1.6 (1.2) * Mean with standard deviation in parentheses.

Results: Cost Sharing

Cost st Sharing ng (effect ect of $10 increase) ase) Any Utilizati ization

• n

TROU OUP P Score e (User sers) s)

Never Dual Dual Never Dual Dual

Acetaminophen/Oxycodone Hydrochloride 0.000 0.013*

• 0.052

0.063 Acetaminophen/Hydrocodone Bitartrate 0.019**

• 0.004

0.077** 0.002 Acetaminophen/Codeine Phosphate

• 0.027*
• 0.021
• 0.028
• 0.099

Hydromorphone Hydrochloride 0.004

• 0.003

0.009 0.029 Oxycodone Hydrochloride 0.000

• 0.002**

0.009*

• 0.023**

Fentanyl

• 0.003*
• 0.000
• 0.010*

0.011* Morphine Sulfate

• 0.003
• 0.009

0.005

• 0.048

Tramadol Hydrochloride 0.000

• 0.004

0.007 0.028 * Significant at p < 0.05 ** Significant at p < 0.01

Results: UM Tools

Utilizati ization n Management gement Tools s (effec ect t of 10% incre rease) se) Any Utilizati ization

• n

TROU OUP P Score e (Us Users ers)

Never Dual Dual Never Dual Dual

Step Therapy 0.004 0.000 0.045

• 0.073

Quantity Limits

• 0.000
• 0.002

0.002 0.009 Prior Authorization

• 0.007
• 0.001
• 0.078**
• 0.041

* Significant at p < 0.05 ** Significant at p < 0.01

Discussion

• Our findings indicate that disabled Medicare beneficiaries are not

responsive to higher opioid cost sharing

– For all opioids, an increase of $10 in cost sharing associated with a 1.7% decrease in likelihood of utilization – statistically significant but small magnitude – Similar small effects when splitting across opioid categories

• Beneficiaries tend to not be responsive to utilization management

tools as well

– Increase in prior authorization associated with reduction in TROUP score (increase of 10% assoc. with reduction of 0.078 points)

Limitations

• Focus on only two states, which may limit generalizability due to

any state-level policies in effect to control opioid use and misuse

– Each state has a large disabled Medicare population

• Low opioid costs may in turn limit the potential variation across

Part D plan cost sharing

• Beneficiaries may switch plans, and may do so to seek lower cost

sharing for opioids

– Conducted analysis that held beneficiaries’ plans constant for all three years to see if switching might have played a role, and found no effect

Policy Considerations

• Lack of responsiveness to cost sharing and UM tools may be due

to three factors:

– Low cost opioid availability – Little variation across Part D plans in cost sharing – Relatively low use of UM tools to control opioid use and misuse in Part D

• Policy options Part D plans might consider

– Charging higher cost sharing for opioids (may not be feasible if opioid is already low-cost) – Imposing more restrictive utilization management tools

This project was funded by a grant from the CDC/NIDA: 1U01CE002497-01

Funding Source

This project was funded by a grant from the CDC/NIDA: 1U01CE002497-01