Haiden A. Huskamp, PhD

Medicare Advantage prescription drug plans (MA-PDs) and standalone prescription drug plans (PDPs) face different incentives for plan design resulting from the scope of covered benefits (only outpatient drugs for PDPs versus all drug and nondrug services for Medicare Advantage [MA]/MA-PDs). The objective is to begin to explore how MA-PDs and PDPs may be responding to their different incentives related to benefit design.

We compared 2012 PDP and MA-PD average formulary coverage, prior authorization (PA) or step therapy use, and copayment requirements for drugs in 6 classes used commonly among Medicare beneficiaries.

We primarily used 2012 Prescription Drug Plan Formulary and Pharmacy Network Files and MA enrollment data. 2011 Truven Health MarketScan claims were used to estimate drug prices and to compute drug market share. Average coverage and PA/step rates, and average copayment requirements, were weighted by plan enrollment and drug market share.

MA-PDs are generally more likely to cover and less likely to require PA/step for brand name drugs with generic alternatives than PDPs, and MA-PDs often have lower copayment requirements for these drugs. For brands without generics, we generally found no differences in average rates of coverage or PA/step, but MA-PDs were more likely to cover all brands without generics in a class.

We found modest, confirmatory evidence suggesting that PDPs and MA-PDs respond to different incentives for plan design. Future research is needed to understand the factors that influence Medicare drug plan design decisions.

We found modest, confirmatory evidence suggesting that stand-alone prescription drug plans (PDPs) and Medicare Advantage prescription drug plans (MA-PDs) respond to different incentives for plan design. MA-PDs are generally more likely to cover and less likely to require prior authorization or step therapy for brand name drugs with generic alternatives than PDPs, and MA-PDs often have lower copayment requirements for these drugs.

This paper provides suggestive evidence that a stand-alone Part D plan has drawbacks resulting from lack of integration with the medical benefit.

This information may inform efforts to regulate Medicare prescription drug plan generosity.

There are 2 types of Medicare Part D prescription drug plans. In 2010, approximately 17.7 million Medicare beneficiaries enrolled in a stand-alone prescription drug plan (PDP), continuing to receive Parts A and B benefits through the traditional Medicare program.

 Approximately 9.9 million beneficiaries enrolled in a Medicare Advantage-Prescription Drug plan (MA-PD), receiving Parts A and B benefits through the qualified Medicare Advantage (MA) plan that sponsored their MA-PD. Due to differences in the scope of benefits covered under PDPs versus MA/MA-PDs, these 2 types of plans face different incentives for drug plan design. Because of the integration of drug and nondrug coverage in MA plans and MA-PDs, MA-PDs have an incentive to provide relatively generous coverage for medications for which the incremental costs of doing so are offset by decreased medical spending over the period of expected enrollment.

 In contrast, PDPs, which are responsible only for drug expenditures, do not reap any efficiency gains with respect to Parts A and B services that may result from more generous drug coverage, and thus have lower incentives relative to MA-PDs to encourage use of drugs that may result in offsets for nondrug expenditures.

Both MA-PDs and PDPs have a choice of offering a plan with the defined standard benefit, a benefit that is actuarially equivalent to the defined standard benefit and consistent with CMS regulations governing coverage, or enhanced benefits. The defined standard benefit for 2013 has a deductible of $325. For spending between $326 and $2970, the plan pays 75% and the enrollee pays 25%. For expenditures between $2970 and $6733.75—known as the coverage gap&mdash;enrollees are responsible for a larger share, with enrollees paying 79% of generic drug expenditures and 47.5% of brand drug expenditures in the gap. For expenditures exceeding the $6733.75 catastrophic coverage limit, Medicare pays 80%, the plan pays 15%, and the enrollee pays 5%.

Previous studies have documented average aggregate (ie, aggregated across all drugs) differences in use of cost sharing and utilization management for PDPs relative to MA-PDs, with MAPDs typically offering more generous coverage.

 For example, in 2012, PDP enrollees faced some form of utilization management (ie, prior authorization [PA] requirements, step therapy requirements, or quantity limits) for 36% of drugs listed on the plan’s formulary while MA-PD enrollees faced utilization management for 31% of formulary drugs.

Also, MA-PDs had a lower average premium, were more likely to have a zero deductible, and were more likely to provide additional benefits in the coverage gap than PDPs.

Little is known about how MA-PDs and PDPs make decisions about coverage and utilization management requirements for specific medications, and data on aggregate differences in coverage levels for the 2 types of plans like the data described above do not shed much light on this complex decision-making process. To begin to explore how MA-PDs and PDPs may be responding to their different incentives around drug benefit design, we compared 2012 PDP and MA-PD average coverage, utilization management, and copayment requirements for specific drugs in several classes of medications used commonly among Medicare beneficiaries.

To identify MA-PDs and PDPs, we used the January 2012 CMS Prescription Drug Plan Formulary and Pharmacy Network Files (hereafter, “CMS formulary files”). These files contain information on plan characteristics as well as National Drug Code (NDC)-level information on formulary coverage, PA and step therapy requirements, and copayment requirements for all MA-PDs and PDPs operating in January 2012, with the exception of employersponsored MA-PDs. We excluded 125 private fee-for-service MA plans, as well as 506 special needs plans, 47 cost plans (ie, plans that Medicare contracts with a cost-reimbursement basis under Section 1876 of the Social Security Act), 61 plans serving US territories, 28 plans with no enrollment as of January 2012, and 1 plan for which formulary information was not available. After these exclusions, there were 1035 PDPs and 1512 MA-PDs in our sample.

Next, we linked the CMS formulary files for these plans with January 2012 plan enrollment information from the MA/Part D Contract and Enrollment Data available on CMS’ website.

To characterize the generosity of drug coverage in MA-PDs versus PDPs, we focused on 6 medication classes used frequently by Medicare beneficiaries for chronic medical conditions and for which at least one generic alternative was available in the class. Because drug plans generally cover all or almost all generic medications generously due to their relatively low cost, we focused analyses on brand name medications. Two of the 6 classes studied (antidepressants and antipsychotics) are Part D “protected” classes, meaning that plans must cover at least 1 formulation of every drug. The other 4 (Alzheimer’s disease medications, angiotensin receptor blockers [ARBs], bisphosphonates, and statins) are nonprotected classes, meaning that plans must cover at least two drugs in the class. Plans are permitted to use utilization management requirements like PA or step therapy to influence medication use in all classes, both protected and non-protected.

We examined 3 primary measures of coverage generosity: 1) formulary coverage; 2) use of PA and/or step therapy, conditional on formulary coverage; and 3) average copayment required for a 30-day prescription. To assess average formulary coverage, we first calculated coverage percentages for each formulation of each drug, collapsing over different NDCs for the same formulation. We expected plan coverage decisions for brand drugs to differ based on whether a generic equivalent of the brand drug was available. We also thought decisions about drugs might differ for brand combination medications (ie, medications that combine two or more molecular entities). Thus, we divided drugs into 3 categories for each class: brand drugs with a generic equivalent available, brand drugs with no generic equivalent available, and combination drugs (of the 19 combination products studied, all but 1 were brands without generic equivalents).

Within each drug class and category, we weighted the coverage percentages by plan enrollment and drug market share within the class, and then calculated the mean coverage percentage for each class and category for PDPs versus MA-PDs. We used 2011 Truven Health MarketScan data, which includes prescription drug claims for over 50 million individuals with employer-sponsored private insurance, to calculate drug market share. By weighting on the basis of drug market share, we “down-weight” observations for rarely-used drugs.

To test for a statistically-significant difference in average coverage between PDPs and MA-PDs, we calculated 

tests of the difference in the mean number of drugs covered for drug categories with more than 1 drug and 

 values from t tests of the difference in proportion covered for categories with a single drug, as well as an F-test of the null hypothesis that all effects are zero. We followed the same process for use of PA/ step therapy.

To calculate the average co-payment requirements for a 30-day prescription, we used plan-level information on the copayment required for each specific NDC from the CMS formulary files. For plans that required coinsurance rather than copayments for a given medication, we instead applied the plan’s coinsurance rate for the initial coverage period (ie, covering expenditures after the deductible is met and before the coverage gap is entered) for a specific NDC from the CMS formulary files to NDC level data on average total price (ie, average of plan plus patient payments per 30-day prescription, not accounting for any rebates negotiated between the plan and the pharmaceutical manufacturer) from the 2011 Truven Health MarketScan data. As for the coverage measures described above, we weighted observations by plan enrollment and drug market share. We calculated 

 values from t tests of the difference in weighted mean copayment requirements for each drug class and category, as well as an F-test of the null hypothesis that all effects are zero.

Finally, we calculated the percentage of MA-PD plans and the percentage of PDPs that covered all brand drugs with no generic equivalents in each class, weighting by plan enrollment but not drug market share, to ensure that we captured differences in covering all formulations, including those that are rarely used.

Brand name drugs with generic equivalents

. Generics accounted for the vast majority of prescriptions filled for drugs with generic equivalents. The average generic market share for these medications ranged from 90.8% for Alzheimer’s medications to 99.7% for statins. On average, MA-PDs were significantly more likely than PDPs to cover brand drugs with generic equivalents in all 4 non-protected classes (Alzheimer’s disease drugs, ARBs, bisphosphonates, and statins) (Table 1).

On average, PDPs covered only 2.5% to 4.6% of brands with generic equivalents for statins, bisphosphonates, and ARBs, and covered 53.5% of brand Alzheimer’s drugs with generic equivalents. MA-PDs covered significantly more of these drugs on average in all classes (12.5% more for Alzheimer’s drugs to 25.6% more for bisphosphonates, all 

 <.0001). For the 2 protected classes, coverage of antipsychotic brands with generics tended to be more generous for MA-PDs vs PDPs, although this did not reach statistical significance (

= .052). There were no statistically significant differences between PDPs and MA-PDs for antidepressants.

Conditional on formulary coverage, MA-PDs were significantly less likely than PDPs to require PA/step therapy for drugs in 3 of 6 classes (Alzheimer’s medications, bisphosphonates, and antidepressants), while MA-PDs were more likely to require PA/step therapy for ARBs than PDPs (Table 1, right columns). There were no statistically significant differences in formulary coverage for MA-PDs vs PDPs for the other 2 classes.

Average copayment requirements per 30-day prescription were lower, on average, for MA-PDs relative to PDPs for 4 classes (ARBs, bisphosphonates, statins, and antipsychotics). Average copayment requirements were higher for MA-PDs relative to PDPs for Alzheimer’s medications, and there was no statistically significant difference between the 2 plan types for antidepressants (

Brand name drugs without generic equivalents

. For brand drugs without generic equivalents, there were no statistically significant differences between MA-PDs and PDPs in either formulary coverage or PA/step therapy (weighted by drug market share, to down-weight drugs that are used rarely), with the exception of statins; MAPDs were significantly more likely than PDPs to require PA/step therapy for statins without generic equivalents (Table 1). However, compared with PDPs, a much larger proportion of MA-PDs covered all brand name medications without generic equivalents for 4 of 6 classes (ARBs, bisphosphonates, statins and antidepressants) (

.) Note that the denominator includes all drugs regardless of market share, unlike Table 1, in which the calculations are weighted by market share). For example, in the Figure, a third (33.5%) of MA-PDs covered all brand ARBs with no generic equivalents while only 5.0% of PDPs did. The Figure does not include antipsychotics (a protected class) or Alzheimer’s medications (not protected) because the proportion of both MA-PDs and PDPs covering all brands with no generics for each class was greater than 90%, allowing limited opportunity to detect coverage differences. The 3 Alzheimer’s medications in this category included the Exelon™ (rivastigmine) patch, which may provide a better delivery route than oral medications for some Alzheimer’s patients, as well as Namenda (memantine) tablets and solution, which have a specific indication for moderate to severe Alzheimer’s, have fewer side effects than the cholinesterase inhibitors, and may be disease-modifying.

Average copayment requirements per 30-day prescription were higher for MA-PDs relative to PDPs for 3 classes (ARBs, bisphosphonates, and statins) and substantially lower for MA-PDs vs PDPs for antipsychotics (Table 2). There was no statistically significant difference in average copayment requirements between MA-PDs and PDPs for Alzheimer’s medications or antidepressants.

. Results were mixed for the brand combination drugs (Table 1). MA-PDs were more likely to cover the bisphosphonate combination (alendronate plus vitamin D) and the antidepressant/antipsychotic combination (olanzapine plus fluoxetine) relative to PDPs. MAPDs were more likely to require PA/step therapy for the statin and bisphosphonate combinations and less likely to require PA/step therapy for the antidepressant/antipsychotic combination than PDPs. Average copayment requirements per 30-day prescription were significantly higher for MA-PDs than PDPs for ARB combination medications and significantly lower for MA-PDs than PDPs for the antidepressant/antipsychotic combination; there were no statistically significant differences in average co-payment requirements for the other 4 classes (Table 2).

F-tests for each of the 3 outcomes (formulary coverage, PA/step therapy, and cost sharing) rejected the null hypothesis that all effects were zero.

Across 6 medication classes used commonly by Medicare beneficiaries, we documented differences in average plan design for MA-PDs and PDPs, with MA-PDs generally offering more generous coverage, and in some cases, substantially more generous coverage. MA-PDs are more likely to cover and less likely to require PA or step therapy for brand name drugs with generic alternatives than PDPs. MA-PDs also often require a lower copayment for these drugs on average relative to PDPs. For brands without generics, we generally found no statistically significant differences in average rates of coverage and PA/ step therapy, but MA-PDs were more likely to cover all brands without generics in a given therapeutic class. Interestingly, average copayment requirements per 30-day prescription for brands without generics were sometimes higher for MA-PDs than PDPs, suggesting that PDPs may be more likely to cover only a subset of brands without generic equivalents but require lower cost sharing for those they do cover relative to MA-PDs. The fact that fewer statistically significant differences between MA-PDs and PDPs were found for the 2 protected classes than for the 4 non-protected classes we studied suggests that the 2 types of plans may be acting in a similar way with respect to the formulary protections afforded to these classes.

There are several possible explanations for why MA-PDs might cover brands with generic equivalents more generously than PDPs. One is that MA-PDs may be less concerned than PDPs about generous coverage of brand name drugs with generic equivalents due to the greater influence over clinical decision making that may result from care management processes and provider network arrangements used by MA plans/ MA-PDs relative to PDPs, which have no direct relationship with clinicians that provide services to individuals covered by traditional Medicare. For example, if a MA plan provides financial incentives or decision support that encourages generic prescribing, or the plan contracts only with clinicians with high generic prescribing rates, network clinicians may rarely prescribe brand drugs even if the drugs are covered.

A second possible explanation for the more generous coverage of brands with generic equivalents by MA-PDs could be that demand for a PDP is more sensitive to a coverage restriction for a particular drug than demand for a given MA-PD. A Medicare beneficiary who decides to join an MA plan with an MA-PD presumably selects a plan by evaluating features of both the MA plan’s nondrug coverage and its MA-PD drug coverage. As a result, if a PDP concerned about adverse selection wishes to discourage enrollment by individuals with a chronic condition associated with high medication costs, imposing coverage restrictions for medications in that class may be more likely to affect PDP enrollment of beneficiaries who use those medications than a similar restriction would affect enrollment for an MA plan and its MA-PD. Hsu and colleagues found that the risk adjustment method used for the Medicare Part D benefit, prescription drug hierarchical condition categories (RxHCCs), tended to overpredict 2006 costs for beneficiaries with low actual costs and to underpredict 2006 costs for those with high actual costs.

 If this pattern had continued since this early research was conducted, MA-PDs might have been more likely to be “overpaid” for relatively healthier enrollees, and thus perhaps better positioned to offer more generous coverage. However, CMS attempted to correct this issue in 2011 by increasing the low income subsidy (LIS) rates and decreasing the non-LIS rates. Based on the findings of Hsu and colleagues, this change may have resulted in an “overcorrection.”

A third possible explanation, although perhaps a less likely one, is that MA-PDs could have a greater incentive than PDPs to offer generous coverage of brand name drugs without generic equivalents if the plan believes that a subset of patients will only adhere to a medication regimen by taking brand name medications and that greater medication adherence in the class can lead to lower total healthcare spending (all else being equal). Enrollees who prefer a brand formulation can apply for a formulary exception allowing them to obtain the brand version at the generic price; no data are available on the extent to which formulary exceptions are granted in this situation, however. Jung, McBean, and Kim found some support for this hypothesis for statin medications.

 They documented statistically higher rates of adherence to statin therapy for MA-PD enrollees relative to PDP enrollees, although they noted that the magnitude of the difference was very small and unlikely to be clinically meaningful. However, Erten and colleagues found similar overall use of guideline- recommended diabetes medications in MA-PDs and PDPs.

 We observed more generous coverage and less cost sharing for brands with generic alternatives in MA-PDs versus PDPs for ARBs, bisphosphonates and statins, as well as lower cost sharing for antipsychotics and antidepressant/ antipsychotic combination brand drugs, all examples where one might expect a greater likelihood of offset onto nondrug expenditures resulting from medication adherence. However, we also observed better coverage for Alzheimer’s medications, a class where an offset seems less likely.

Some of the observed differences in generosity for MA-PDs and PDPs result from the fact that MA plans with premium bids below the county benchmark receive rebates from the federal government that can be used to lower their monthly Part D premium, enhance their drug benefits, or lower drug cost sharing. However, these MA plans can instead choose to use these rebates to lower their Part B premiums, enhance nondrug benefits, or lower nondrug cost sharing, so more generous drug coverage for MA-PDs is not a given.

There are several limitations to our analysis. First, we are unable to observe all factors influencing plan design decisions for PDPs and MA-PDs. We are only able to make inferences based on observed plan design differences. Second, one might expect to see different patterns of plan design for classes that have not experienced generic entry relative to classes with at least one generic option. We were unable to examine this issue because nearly all of the most common classes utilized by Medicare beneficiaries now have at least one generic alternative. Third, our analysis may underestimate differences in MA-PD and PDP coverage to the extent that a subset of MA-PDs may have the same formulary as a PDP (and vice versa) due to common ownership. Finally, drug market share may differ in privately insured populations included in the MarketScan database and Medicare Part D plans, although many of the largest PDPs are sponsored by large pharmacy benefit managers and insurers that use the same formulary and utilization management tools for both their commercial and Part D plans.

We found modest confirmatory evidence suggesting that PDPs and MA-PDs respond to the different incentives around plan design that they face as a result of the scope of their benefits: only outpatient prescription drugs in the case of PDPs and all covered drug and nondrug healthcare services in the case of MA plans offering an MA-PD. MA-PDs are generally more likely to cover and less likely to require PA or step therapy for brand name drugs with generic alternatives than PDPs, and MA-PDs often have lower copayment requirements for these drugs. For brands without generics, we generally found no differences in average rates of coverage and PA/step therapy, but MA-PDs were more likely to cover all brands without generics in a given therapeutic class. Future work is needed to understand the factors that influence Medicare drug benefit design decisions among Part D plans, the impact of those decisions on outcomes, and how Medicare beneficiaries select from among MA-PD and PDP options.

 From From Harvard Medical School, Department of Health Care Policy, Boston, MA, (HH, NK, JD, MC, JN); Brigham and Women’s Hospital, Department of Medicine, Boston, MA, (NK); Harvard University, John F. Kennedy School of Government, Cambridge, MA, and Harvard University, School of Public Health, Boston, MA (JN).

 This research was funded by the National Institute on Aging (#PO1 AG032952).

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.

 Concept and design (HH, NL, MC, JN); acquisition of data (HH, NL, MC, JN); analysis and interpretation of data (HH, NK, JD, MC, JN); drafting of the manuscript (HH, JD); critical revision of the manuscript for important intellectual content (HH, NK, JD, MC, JN ); statistical analysis (HH, NK, JD, MC, JN); provision of study materials or patients (HH, NK, JD, MC, JN); obtaining funding (HH, NK, MC, JN); administrative, technical, or logistic support (JD); supervision (HH).

 Haiden A. Huskamp, Ph.D., Harvard Medical School, Department of Health Care Policy, 180 Longwood Avenue, Boston, MA 02115. E-mail:huskamp@hcp.med.harvard.edu.1. Medicare Payment Advisory Commission. A databook: health care spending and the Medicare program: June 2012. Washington, DC: Medicare Payment Advisory Commission; 2012:155-181.. http://www .medpac.gov/documents/Jun12DataBookEntireReport.pdf.

2. Congressional Budget Office. Offsetting effects of prescription drug use on Medicare’s spending for medical services, November 2012. http://www.cbo.gov/sites/default/files/cbofiles/attachments/43741- MedicalOffsets-11-29-12.pdf

3. Polinski JM, Mohr PE, Johnson L. Impact of Medicare Part D on access to and cost sharing for specialty biologic medications for beneficiaries with rheumatoid arthritis. Arthritis Rheum. 2009;61(6):745-54.

4. Frakt AB, Pizer SD. A first look at the new Medicare prescription drug plans. Health Aff (Millwood). 2006;25(4):W252-261.

5. Kornhuber J, Weller M, Schoppmeyer K, Riederer P. Amantadine and memantine are NMDA receptor antagonists with neuroprotective properties. J Neural Transm Suppl. 1994;43:91-104.

6. McShane R, Areosa Sastre A, Minakaran N. Memantine for dementia. Cochrane Database Syst Rev. 2006;(2):CD003154.

7. Jung K, McBean AM, Kim JA. Comparison of statin adherence among beneficiaries in MA-PD plans versus PDPs. J Manag Care Pharm. 2012; 18(2):106-115.

Articles

Medicare-Advantage Prescription drug plans (MA-PDs) and standalone PDPs appear to respond to different incentives for plan design.

Drug Plan Design Incentives Among Medicare Prescription Drug Plans

Much has been written about trends in Medicare Part D formulary design and consumers’ choice of plans, but little is known about the magnitude of claims rejections or their clinical and administrative implications. Our objective was to study the overall rate at which Part D claims are rejected, whether these rates differ across plans, drugs, and medication classes, and how these rejection rates and reasons have evolved over time.

We performed descriptive analyses of data on paid and rejected Part D claims submitted by 1 large national longterm care pharmacy from 2006 to 2010. In each of the 5 study years, data included approximately 450,000 Medicare beneficiaries living in long-term care settings with approximately 4 million Part D drug claims. Claims rejection rates and reasons for rejection are tabulated for each study year at the plan, drug, and class levels.

Nearly 1 in 6 drug claims was rejected during the first 5 years of the Medicare Part D program, and this rate has increased over time. Rejection rates and reasons for rejection varied substantially across drug products and Part D plans. Moreover, the reasons for denials evolved over our study period. Coverage has become less of a factor in claims rejections than it was initially and other formulary tools such as drug utilization review, quantity-related coverage limits, and prior authorization are increasingly used to deny claims.

 Examining claims rejection rates can provide important supplemental information to assess plans’ generosity of coverage and to identify potential areas of concern.

Our findings suggest that medication-specific and planwide rejection rates could be useful information, especially for policy makers and clinicians.

		Policy makers could monitor rejection rates across plans to identify potential access problems for Medicare beneficiaries, to inform the Part D appeals process, and to make adjustments to regulatory guidance.

		More specific to the nursing home population, policy makers could consider using rejection rates in decisions about plan assignment for dually eligible residents or which plans are eligible to serve dually eligible individuals.

		Similarly, providers and pharmacies could monitor rejection data in establishing formularies and prescribing practices more generally.

The US government relies on private plans to administer the Medicare Part D prescription drug benefit, including the establishment of premiums, pharmacy networks, and formulary design. Within limits, Part D plans (PDPs) have flexibility to shape enrollees’ drug use through levers such as coverage, cost-sharing, and utilization management techniques including prior authorization, step therapy, and quantity limits. The underlying expectation of this approach is that informed consumers will choose the plan that best suits their needs and that price competition across plans will optimize government payments for drugs.

Although Part D includes special protections for nursing home residents, the program’s administrative reliance on private plans and the emphasis on consumer choice is similar across institutional and community settings. In addition to nursing home residents having relatively high levels of medication use and physical and cognitive frailty as compared with other Medicare beneficiaries, a key difference in the nursing home setting is that almost two-thirds of long-stay residents are dually eligible for Medicare and Medicaid.

 Part D’s implementation shifted medication coverage for duals from Medicaid to Medicare and randomly assigned these individuals to plans with premiums at or below regional benchmark rates set by the Centers for Medicare & Medicaid Services (CMS). Under Part D, nursing homes and the pharmacies with which they contract no longer function under a state’s Medicaid policies for dually eligible residents but instead work across multiple plans, each of which may have different formulary designs and administrative procedures.

 The transition to Part D in the nursing home sector has not always been smooth, with early concerns about coverage adequacy and administrative burdens expressed by physicians, pharmacists, and administrators working in nursing homes.

Little has been written about the extent of claims rejections or their clinical and administrative implications. One previous study using data on claims in rejected status at the end of 2006 found considerable variation in the reasons for rejection across medications and in the relative rejection rates across PDPs.

 However, no published information exists about the overall rate at which Part D claims are rejected; whether these rates differ across plans, drugs, and medication classes; and how these rejection rates and reasons have evolved over time. To address these questions, we examined data on paid and rejected Part D claims submitted by 1 large national long-term care pharmacy (LTCP) over the initial 5 years of Medicare Part D.

We obtained data on all paid and rejected Part D claims from Omnicare, Inc (Covington, Kentucky), the nation’s largest long-term care pharmacy, operating in 47 states and serving approximately half of US nursing home residents. A claim is defined as a demand of payment for a particular drug, for a particular individual, on a particular date. Data include Part D claims for nursing home and assisted living residents, although more than 8 in 10 claims are estimated to be for nursing home residents. Data capture all claims filed in the month of March for each of 5 study years (2006-2010) (although we purposely avoided using data from January because of potential transition challenges at the beginning of the year, the choice of March was somewhat arbitrary. March 2010 also was the latest month available when the study began). All observations include the claim date; unique identifiers for residents and facilities; the National Drug Code (NDC) of the product; the plan to which the claim was submitted; and up to 3 reasons for denial of rejected claims. For ease of presentation, we group rejections into 3 broad categories (see 

): 1) Product not covered—capturing instances where the product is not covered; 2) utilization management techniques&mdash;capturing instances where coverage applies only after plans approve necessary documentation from pharmacies and clinicians (prior authorization), where less expensive medications must first be tried and failed before more expensive medications are dispensed (step therapy), and instances where plans limit the number (or amount) of drugs covered within a certain time period (quantity limits and refill too soon); and 3) administrative rejections&mdash;including instances where claims have non-matched pharmacy or member identification numbers and missing/invalid information about the prescriber, patient, or prescription itself. Importantly, administrative rejections can stem directly from coverage restrictions (eg, missing or inadequate justification for a “dispense as written” prescription order is the second-most prominent code in this category).

We define the total number of claims as the sum of paid claims and the subset of rejected claims that remain unpaid at the end of the month. We are able to flag claims that were rejected multiple times (around 1 in 4 rejected claims) and claims that were rejected and then paid during the 1-month windows of the 5 study years. We count these flagged claims only once. We define the total number of rejections to include all rejected claims, regardless of whether the claim was paid subsequently. If a claim was rejected multiple times for the same reason(s), we count these as 1 rejection. If a claim was rejected multiple times for different reasons, we count each unique set of reasons separately. We are unable to observe the life cycle of claims outside our 1-month windows (eg, if a claim was rejected in March and paid in a subsequent month, we have no record of the later payment). To calculate the rejection rate, we divide the total number of rejections by the total number of submitted claims. We present the rejection rate by year, PDP, and product. We consider generic and brand formulations of the same molecule as distinct products for our analyses; similarly, we treat different formulations of the same molecule (eg, tablets, solutions, and extendedrelease formulations) separately. We exclude 248,026 rejections (7% of all rejections) that were rejected due to problems transmitting claims electronically, such as “host processing error” or “system unavailable.” Based on conversations with the data provider, these rejections typically are resubmitted automatically, either to be resolved or rejected for another reason. We also exclude from our analyses the small number of claims that could not be matched to a drug name (1.6% of rejected claims; 0.1% of paid claims) and that were missing a rejection reason (0.02%).

Our data include approximately 450,000 unique individuals and 4 million total claims in each of the 5 study years (

). The overall rejection rate ranged from 14% to 19% over the 2006 to 2010 time frame, increasing slightly in recent years. The percent of rejections due to products not being covered declined considerably over the study period, from 21% of all rejections in 2006 to 10% in 2010. In contrast, rejections due to utilization management techniques such as prior authorization, drug utilization review, and other coverage restrictions grew in prominence, from 33% of all rejections in 2006 to 44% in 2010. Within the utilization management category of rejections, the most prominent subcategories of rejections were “refill too soon” (43% of utilization management rejections), “Drug Utilization Review Reject Error” (30%), “plan limitations exceeded” (17%), and “prior authorization required” (8%) (eAppendix A). Administrative rejections were consistently high over the study period (approximately 43% of all rejections, on average). Specific codes comprising most administrative rejections were non-matched pharmacy numbers (31% of administrative rejections); missing/ invalid information for requests to “dispense as written” (ie, requests to fill prescriptions for brand-name drugs, even though generic substitutes might be available) (8%); nonmatched plan member numbers (7%); and missing date of service (6%).

details product-level rejections for our most recent year of data (2010), with the top and bottom panels describing rejections for the most prescribed medications and medications with the highest rejection rates among drugs with at least 5000 claims, respectively. Among the 20 most prescribed drugs (13 of which are generics), the rejection rate was between 13% and 19%, with denials generally divided between administrative rejections and utilization management, and rarely due to lack of coverage. For the 20 most commonly rejected drugs (9 of which are generics), the rejection rates (23%-62%) and reasons for denial varied more widely. Lack of coverage factored more prominently into these rejections, especially for some alternate formulations. Administrative rejection codes accounted for more than half of denials for 10 out of 20 medications.

 shows rejection rates over time for 7 classes commonly used in long-term care settings—antidepressants, angiotensin receptor blockers, atypical antipsychotics, cholinesterase inhibitors, long-acting opioids, nebulized inhalants, and osteoporosis medications (

 lists drugs by class). Other than the rejection rates for nebulized inhalants, which declined from 2008 to 2010, class-level rejection rates declined initially and then increased in subsequent years. Some increases were relatively large—for instance, after falling to a low of 16% in 2007, the rejection rate for long-acting opioids increased to 28% in 2010. Rejection reasons varied across classes (see 

displays rejection rates and the reasons across plans with higher claims volume in 2010. If a company had multiple PDPs nationwide, the information is aggregated across these plans. The overall rejection rates varied (6%-30%), as did the distribution of rejection reasons. Among plans with higher rejection rates, administrative rejections were relatively prominent. The 3 plans with the highest proportion of rejections for administrative reasons (74%, 68%, and 65%) were among the 3 plans with highest rejection rates overall (30%, 24%, and 29%, respectively). Translated to an administrative rejection rate (overall rejection rate x proportion of rejections for administrative reasons), the rate at which claims were rejected because of administrative reasons generally was between 2% and 9% of claims (not shown). Relatively high rates of administrative rejections arose in smaller and larger plans alike. The rates of claims rejections in the other 2 categories did not convey anything consistent about plans’ overall rejection rates.

Over the initial 5 years of Medicare Part D, nearly 1 in 6 drug claims for beneficiaries living in nursing homes and other long-term care settings was rejected. Although one might have expected the rejection rate to decline over time after the initial transition to the program and as pharmacies and clinicians grew more accustomed to working across private plans, this has not occurred. After an initial decline, the rejection rate has increased slightly each year. At the same time, reasons for denials have evolved considerably. Lack of coverage has become less of a factor in claims rejections than it was initially; increasingly, other formulary tools such as drug utilization review, quantity-related coverage limits, and prior authorization are used to deny claims. Although these results are consistent with broader trends concerning PDP formularies, somewhat more surprising is the persistence of administrative claims rejections throughout the study period, a feature that likely reflects the complexity of working across multiple plans and policies in a given year, as well as changes in coverage policies over time.

Not surprisingly, rejection rates and reasons varied considerably across products. Medications most commonly used by nursing home residents generally have below average denial rates (around 1 in 7 claims), with coverage issues rarely noted. Given the relatively low cost of many of these drugs (9 out of 10 most commonly used drugs are generics), these features are unsurprising. Among drugs with the highest rejection rates, explanations for denials are complex and vary across products, which include generics, brand drugs with generic alternatives, and brand drugs without generic alternatives.

Among the most rejected generics, half of the denials for pantoprazole sodium extended release tablet (a proton pump inhibitor for conditions such as gastroesophageal reflux disease) were due to the product not being covered. The primary reason for rejections of fluticasone propionate nasal spray (for asthma and allergy symptoms) stemmed from claims having missing/invalid information about days-supply, a problem also affecting other solutions and inhalation products. Three-fourths of rejections for potassium chloride solution (for nutritional deficiencies) were due to non-coverage, a feature likely reflecting a CMS ruling that the formulation did not meet Part D coverage requirements and the subsequent shift by PDPs to reimburse only non-liquid formulations of the drug.

Denials for brand name drugs with generic equivalents likely reflect PDPs’ efforts to promote use of cheaper generic alternatives. The high rejection rate for Flomax (tamsulosin) (for symptoms of an enlarged prostate) largely stemmed from claims having missing/invalid “dispense as written” information, an issue that presumably arose following the availability of generic tamsulosin in early 2010. Among the handful of brand drugs without generic equivalents, an example worth highlighting is Procrit (epoetin alfa), an expensive blood modifier used to treat anemia due to chronic renal failure and other select conditions. More than three-fourths of Procrit’s rejections were due to prior authorization issues, something not unexpected given the drug’s high cost and potentially harmful side effects.

A striking feature of our product-level findings is that 6 out of 20 medications with the highest rejection rates in 2010 are used to treat pain (oxycodone/acetaminophen, oxycodone hydrochloride, morphine sulfate extended release, fentanyl patch, propoxyphene napsylate/acetaminophen, and Lidoderm (lidocaine). With the exception of propoxyphene napsylate/acetaminophen (an opioid combination of questionable effectiveness and safety in older adults

) and Lidoderm (an expensive topical anesthetic patch used to treat pain from shingles and other sources of neuropathic pain), denials for pain medications arise primarily from administrative rejections and utilization management techniques. An illustrative example is that nearly 1 in 3 claims was denied for the fentanyl patch in 2010—a rate nearly double its 2006 level; and administrative rejections and utilization management accounted for almost all of these rejections. Although rejection rates for pain medications could reflect their considerable abuse and diversion potential, concerns must be weighed against the need for timely access to effective medications for individuals living with chronic pain.

Rejection rates and reasons also vary considerably across PDPs. In the context of a facility’s residents being enrolled in multiple plans, this variation presents administrative challenges for facility and pharmacy staff and may undermine the predictability of coverage across residents with similar conditions. Although it is difficult to convey a clear, concise story in the context of the PDP rejection rates, plans’ higher administrative rejection rates generally imply higher rejection rates overall. Further context for administrative rejections relates to continuing challenges that providers and pharmacies face in updating residents’ Medicaid eligibility status, delays which can result in enrollment gaps and incorrectly collected copayments.

Nonetheless, the cross-plan variation in the extent to which these issues lead to rejected claims implies that there may be room for improvement at plans with especially high administrative rejection rates.

It is unclear what the optimal or expected rejection rate should be in Part D generally or in the long-term care setting specifically. Besides administrative errors that must be corrected before payment, plans have legitimate reasons to deny claims; and the optimal rejection rate is not zero. Prior  authorization, for instance, could add valuable safeguards when prescribing is of questionable efficacy or appropriateness (eg, in the historically problematic area of psychotropic drug use among elderly nursing home residents

). Similarly, step therapy edits could push clinicians to prescribe less costly medications that are clinically similar or, in the case of brand-to-generic comparisons, equivalent. Nonetheless, use of these strategies creates a tension between the push to lower costs and the importance of ensuring access to needed medications.

The clinical impact of claims rejections on long-term care residents is unclear. We do not have information about beneficiaries’ diagnoses or health outcomes and are unable to assess the potential impact of denials on individuals’ health or on the overall appropriateness of prescribing. Similarly, we do not have information about the absolute or relative administrative burden or costs associated with rejected claims. It is likely that some denials are rectified relatively easily, while others require more coordinated effort. Because of the limited time frame (1 month each year) for which we have data, we are unable to estimate the extent to which individuals switch or discontinue medications because of claims denials (or, conversely, obtain their needed medication in the end). Prescribing behavior also may adjust to PDPs’ formularies and administrative practices (eg, if clinicians reduce prescribing a particular medication because it is difficult to obtain), something we are unable to observe. Important context for these points is that regulations require that nursing homes adhere to residents’ clinical care plans regardless of financial coverage, meaning that clinicians must either work within plans’ extant formulary constraints or leave facilities and/or pharmacies to shoulder the financial cost of medications. Finally, although the same PDPs serve individuals across institutional and community-based settings, our data are from individuals living in long-term care settings and come from a single (albeit very large) LTCP. Thus, generalizing our findings to the Part D program as a whole or even to the entire long-term care sector should be done with caution.

Nonetheless, our results have several implications for policy. Our findings suggest that medication-specific and planwide rejection rates could be useful information for policy makers, beneficiaries, and clinicians. Policy makers could monitor and possibly report rejection rates across plans to identify potential access problems for Medicare beneficiaries, to inform the Part D appeals process, and even to make adjustments to regulatory guidance if necessary. Given uncertainty about the optimal or appropriate rejection rate in Part D, related oversight likely would focus on identifying outliers at the plan, product, or class levels for further investigation. For example, CMS currently requires that plans cover at least 1 formulation of all medications in selected medication classes.

 CMS could consider rejection rates in decisions about whether to incorporate special protections around formulary coverage or the use of utilization management for these or other drugs. Although beneficiaries could use rejection rates in evaluating plans (eg, to compare plans with comparable coverage on drugs of interest), previous research suggests consumers already are overwhelmed by available information and could benefit from having fewer, more targeted data points.

 More specific to the nursing home population, policy makers could consider using rejection rates in decisions about plan assignment of dually eligible residents (eg, not assigning individuals to plans where medication access looks especially problematic) or even about which plans are eligible to serve dually eligible individuals altogether. Similarly, providers and the pharmacies they work with could monitor rejection data in establishing formularies and prescribing practices more generally.

Throughout the brief history of the Medicare Part D program, comparing generosity of coverage across private PDPs has been a key point of interest for consumers, policy makers, and researchers.

Claims rejections have been an unobserved feature of these comparisons to date. Our research shows that examining rejection rates across plans and medications can provide important supplemental information to assess plans’ generosity of coverage and to identify potential areas of concern. Going forward, information about claims rejections should be more systematically monitored and analyzed for oversight purposes, not only to identify administrative challenges that arise in the Part D marketplace but also to ensure the program is working well for Medicare beneficiaries as a whole.

From Department of Health Care Policy (DGS, HAH), Harvard Medical School, Boston, MA: Department of Health Services, Policy, and Practice (LK), Alpert Medical School of Brown University, Providence, RI; Hebrew SeniorLife (SLM), Institute for Aging Research, Boston, MA; Omnicare, Inc (BJZ), Livonia, MI.

 This work was supported by a grant from the National Institute on Aging (NIA), grant number R01 AG034085. Dr Stevenson also was supported by an NIA Career Development Award (K01 AG038481). Dr Huskamp also was supported by a Robert Wood Johnson Foundation Investigator Award in Health Policy Research. Dr Mitchell was supported by NIA grant number K24AG033640.

This study presents data on paid and rejected claims submitted by 1 large long-term care pharmacy over the initial 5 years of Medicare Part D.

Medicare Part D Claims Rejections for Nursing Home Residents, 2006 to 2010

To evaluate the impact of switching from an HMO to a high-deductible health plan on the costs and utilization of maternity care.

 We compared 229 women who delivered babies before or after their employers mandated a switch from HMO coverage to a high-deductible health plan, with a control group of 2180 matched women who delivered babies while their employers remained in an HMO plan. Administrative claims from a large Massachusetts-based health insurance program were used in a difference-indifferences regression analysis.

Mean out-of-pocket maternity care costs for high-deductible group members increased from $356 for women who delivered before the insurance transition (n = 86) to $942 for women who delivered after the transition (n = 143), compared with a change from $262 (n = 711) to $282 (n = 1569) for HMO members, a relative increase of 106% (P <.001) for high-deductible members. Delivery after transition to a high-deductible plan was not associated with changes in the odds of receiving early prenatal care (odds ratio [OR], 1.02; 95% confidence interval [CI], 0.32-3.19), recommended prenatal visits (OR, 1.64; 95% CI, 0.89-3.02), or postpartum care (OR, 0.74; 95% CI, 0.42-1.32).

 Switching from an HMO to a high-deductible plan with exemptions for routine care increased out-of-pocket member costs for maternity care, but had no apparent adverse impacts on receipt of recommended prenatal and postpartum care.

	Transition from an HMO to a high-deductible health plan with deductible exemptions for routine preventive maternity services was associated with:

		Substantial increases in out-of-pocket expenses for maternity and delivery-related care (although information on changes in employer account contributions and employee premium contributions was not available).

		A statistically insignificant (but large) reduction in overall costs.

		No apparent adverse impacts on recommended prenatal and postpartum care.

Childbirth is the leading reason for hospitalization in the United States.

 For both private insurers and Medicaid, hospital maternity and newborn charges exceed those for any other condition.

 In US hospitals, vaginal and caesarean deliveries cost approximately $8000 and $11,000 respectively.

High-deductible health plans (HDHPs) have expanded rapidly in recent years, with 27% of workers having a deductible of at least $1000.

 Between 2006 and 2010, enrollment in HDHPs tripled to 10.0 million members,

 and analysts predict an “explosion” in growth due to the recent health reform legislation.

Although HDHPs are intended to decrease escalating health costs and discretionary care, they have generated controversy.

 High cost sharing can reduce appropriate  healthcare utilization, including hospitalizations, preventive services, and essential medicines.

 Therefore, HDHPs often fully cover preventive services, including prenatal care.

 No previous studies have measured the impact of HDHPs on the costs and use of maternity care.

Our study examined this question among women insured by a large Massachusetts-based health insurance program (Harvard Pilgrim Health Care) that predominantly insures members through HMO plans and began offering HDHPs in April 2002. We anticipated that HDHP membership would increase out-of-pocket maternity expenditures, shift costs to members, and possibly reduce total maternity care expenditures, consistent with the cost-containment goal of consumer-directed healthcare.

Although use of exempted services might be expected to remain stable, we hypothesized 3 mechanisms that could decrease utilization. (1) Cost sharing for nonexempt services (such as laboratory tests) ordered during prenatal visits could discourage subsequent visits. (2) The intricacy of maternity benefit design in HDHPs (eg, deductible exemptions based on clinical conditions) combined with uncertainty regarding costs of anticipated services could cause confusion and reduced care even for exempt services.

 (3) Experiencing a previous large deductible payment (eg, an emergency department visit) might induce “sticker shock” and broadly reduce utilization.

The objective of this study was to evaluate the impact of transition from an HMO to an HDHP on the costs and utilization of maternity and delivery care services within the 6 months before and 3 months following delivery.

The Office for Sponsored Programs (Harvard Pilgrim Health Care Institute) reviewed this study and granted institutional review board approval.

We used a pre—post study design with matched control group and a difference-in-differences analysis to measure changes in study outcomes.

Harvard Pilgrim Health Care is a health plan serving approximately 1 million individuals in New England. On April 1, 2002, Harvard Pilgrim began offering HDHPs with annual deductible amounts of $500 to $2000 for individuals and $1000 to $4000 for families. Members of family plans also had individual deductibles equal to half of the family deductible. Of Harvard Pilgrim’s commercially insured members, approximately 70% have HMO plans, 20% have preferred provider organization (PPO) plans, and 10% have HDHPs, compared with corresponding national rates of 58%, 19%, and 13%.

 Although PPO plans are more common than the HMO plans we study, cost-sharing requirements for services such as preventive tests, hospitalizations, and specialist visits are quite similar4; the primary differences between HMO and PPO plans are the more limited provider network, the need for specialist referrals, and the inclusion of high deductibles in approximately 40% of PPO plans.

Insurance coverage for maternity care in Harvard Pilgrim HMO plans includes full coverage of prenatal and postpartum care, with no cost sharing beyond office visit and  hospitalization copayments. Outpatient visit copayments among HMO members in our study ranged from $5 to $25 (median $15) while hospitalization copayments ranged from $0 to $1000 (median $250).

In the HDHPs we studied, many maternity care services are exempt from the deductible, having either first dollar coverage or low copayments. First dollar coverage applies to routine prenatal and postpartum visits, fetal ultrasounds (sonograms),  routine urinalysis, Papanicolaou tests, and screenings for sexually transmitted infections. All hospital delivery charges and many outpatient procedures are subject to the deductible, but are covered in full after the deductible has been met. Copayments of $20 apply to non—maternity care outpatient visits, urgent care visits, and specialist consultations. These copayments apply regardless of whether the member has exceeded the deductible spending level. However, the HDHPs have an out-of-pocket maximum (including copayments and deductibles) of $2000 to $4000 for individuals and $4000 to $8000 for families. After a member reaches the out-of-pocket maximum, all services are covered in full. Provider networks for women in HDHPs and HMO plans are identical.

The HDHPs we studied were not eligible to be paired with health savings accounts (HSAs); all were eligible to be combined with health reimbursement accounts (HRAs). Although we could not account for HRA purchases from other companies, only 3% of the HDHPs we studied had HRAs through Harvard Pilgrim. Nationally, fewer than half of HDHPs with deductibles over $1000 are paired with HSA or HRA accounts,

 so our analysis is relevant for the predominant type of HDHP.

 we created a cohort of health plan members enrolled through employers who exclusively offered insurance through Harvard Pilgrim Health Care. We defined 2 cohorts: an HDHP group and an HMO control group. The HDHP group included members enrolled in traditional HMO plans during a 1-year baseline period prior to an employer-mandated switch to an HDHP. The date of this switch was defined as the index date. For each HDHP member, we identified 8 contemporaneous members who  were continuously enrolled in traditional HMO plans during the same time period. We assigned HMO controls the same index date as their HDHP counterparts, and the distribution of index dates was similar in the HDHP and HMO control groups in the final study population. We selected only HMO members whose employers did not offer an option to enroll in an HDHP or any other plan types. Therefore, no study members were able to self-select their health insurance plan or benefits structure.

From the HDHP and HMO groups, we identified women who had given birth using 

International Classification of Diseases, Ninth Revision

 diagnosis codes and diagnosis-related group payment codes. We further divided the study cohorts based on whether each woman delivered a baby prior to or following the index date. The final study population included 2409 women who delivered babies between October 1, 2001, and November 30, 2007. This population included 229 women in the HDHP group (86 delivered prior to the index date and 143 after) and 2180 women in the HMO control group (711 delivered prior to the index date and 1569 after). Among women in the HDHP group, 95 had a $500 deductible, 92 had a $1000 deductible, and 34 had a $2000 deductible (values unavailable for 8 women). The study used data from 6 months prior to and 3 months following delivery (the second and third trimesters of pregnancy and early postpartum period).

Study outcomes included costs, quality, and utilization, categorized as delivery related (for care provided during the delivery hospitalization) and maternity related (for all types of maternity care, including delivery-related care). Cost measures included out-of-pocket and total expenditures. Out-of-pocket costs comprised all member expenditures toward copayments and deductibles. Total costs were calculated as the sum of the expenditures by the insurer (payer costs) and out-of-pocket expenditures by the member.

We calculated maternity and delivery-related costs using a previously established set of diagnosis, procedure, and payment codes that included prenatal and postpartum visits,  obstetrical anesthesia, introduction and repair services, vaginal or caesarean delivery, radiology procedures, pathology and laboratory expenses, and in utero procedures.3 We distinguished delivery services from non—delivery-related maternity care both by the specific service and procedure codes and by the timing of occurrence during the delivery hospitalization. Measures of maternity care quality included 3 measures based on Healthcare Effectiveness Data and Information Set (HEDIS) indicators (early prenatal care, appropriate frequency of prenatal care, and timely postpartum care).25 Early prenatal care was defined as having at least 1 prenatal visit within 42 days of the second trimester. Appropriate frequency of prenatal care comprised at least 7 prenatal visits in the 190 days prior to delivery, as per consensus recommendations.26 We calculated this measure only among women who delivered full-term babies (>37 weeks of gestation) due to lack of information on gestational age at delivery for preterm infants. Timely postpartum care was achieved by having a visit between 21 and 56 days after delivery. General maternity care utilization measures included the total number of prenatal visits and sonograms, and the length of stay for the delivery hospitalization.

Other variables were age at delivery, month of delivery, type of delivery (vaginal or caesarean), gestational diabetes, high-risk pregnancy, preterm delivery, chronic disease score, socioeconomic status, and employer size (number of employees). Clinical conditions were defined using payment or diagnosis codes. As a measure of comorbidity and  care-seeking behavior, we computed the chronic disease score, which predicts future healthcare spending based on pharmacy claims in the baseline year.27 To develop a measure of socioeconomic status, we linked members’ residential addresses to their 2000 US Census block group and created measures of neighborhood education levels and poverty status based on previously established standards.28 Low socioeconomic status was indicated by living in a census block group with either more than 25% of adults having less than a high school education or more than 10% of households living below the poverty level.

We used regression models in a difference-in-differences analysis to assess the independent effect of switching to an HDHP on costs, quality, and utilization outcomes, after controlling for the covariates listed above. This analysis used 3 types of regression models: log-linear for costs, logistic for dichotomous quality measures, and linear for utilization measures (number of visits or days). We also conducted several sensitivity analyses to test the stability of results. Analyses excluding socioeconomic status (which is measured at the census block level rather than the individual level) and adjusting for inflation (using the medical component of the Consumer Price Index) had no impact on the direction or significance of the original findings.

All statistical analyses used 2-tailed tests and a levels of .05, and were conducted using SAS version 9.1 (SAS Institute Inc, Cary, NC).

Study groups were similar, with no statistically significant differences detected (

). The average age at delivery was approximately 33 years. Between 15% and 18% lived in low socioeconomic status neighborhoods. More than 60% worked for small employers. Approximately one-third of women had a caesarean delivery, and between 20% and 30% experienced complications of high-risk pregnancy. Preterm deliveries accounted for fewer than 10% of births, and rates of gestational diabetes were low. Chronic disease scores were consistent across groups and indicate a generally healthy population.

presents costs, quality, and utilization outcomes in the study groups as well as regression-based estimates of the impact of switching from an HMO to an HDHP on each of these outcomes. Regression coefficients for cost outcomes may be interpreted as the estimated percentage change in cost. Mean total maternity care costs for HDHP members  were $9761 for women who delivered prior to the insurance transition and $9997 for those who delivered after the transition, compared with a change from $9731 to $10,434 for HMO members, a 12% (

 = .114) relative reduction in the HDHP group, after adjusting for covariates. This occurred despite a significant increase in the underlying cost trend over time (8%, 

). Further, the HDHP group experienced a 15% relative decline in delivery costs 

In contrast to overall costs, out-of-pocket spending increased following the transition from an HMO to an HDHP. Average out-of-pocket maternity care expenditures among HDHP members rose from $356 before the HDHP transition to $942 for those who delivered after, compared with nearly constant values ($262 to $282) for women who remained inHMO  plans, a relative increase of 106% (

 <.001). Mean out-of-pocket costs for delivery care rose from $258 to $547 among HDHP members, compared with $188 to $218 among HMO members, a 45% relative increase (

 <.001). Average insurer expenditures decreased for women who delivered babies after HDHP enrollment, falling from $9405 to $9055, while rising from $9469 to $10,152 for the control group, a 21% relative decrease (

 = .005). The relative decrease in insurer expenditures for delivery care was similar (22%, 

Achievement of quality standards for early and frequent prenatal care as well as timely postpartum care did not vary significantly across study groups (Table 2). Nearly all women received early prenatal care, and the majority accessed recommended prenatal and postpartum care. Likewise, maternity care utilization was similar across groups, with an average of 9 to 10 prenatal visits and 7 to 8 sonograms (mode = 4; 25% of women had >10 sonograms) and an average delivery hospitalization stay of about 4 days (3 days for vaginal deliveries; 5 days for caesarean deliveries).

As expected, pregnancy and delivery complications, high chronic disease scores, and caesarean and preterm deliveries were generally associated with higher costs and utilization (

This study is the first to characterize the impact of HDHPs on maternity care services. Our findings demonstrate that transitioning from an HMO to an HDHP was associated with increased out-of-pocket costs but stable utilization and quality for maternity and delivery care. Out-of-pocket costs represented a small percentage of total maternity costs for both HMO (4%) and HDHP (11%) members and were in the same range as national averages (6% of total costs).

Nevertheless, HDHP members faced out-of-pocket costs 3 times higher than HMO members but received recommended prenatal and postpartum care at similar rates.

An overall reduction in costs associated with better consumer choice is one of the goals of HDHPs and other consumer-driven healthcare models.

 We found a 12% (nonsignificant) decrease in the average costs of maternity-related care following transition to an HDHP. This trend might result from reductions in expensive individualized services or elective tests and procedures (such as prenatal genetic counseling or early screening for chromosomal abnormalities). Transitioning from an HMO to an HDHP was not associated with adverse impacts on basic prenatal and postpartum quality measures, lending support to deductible exemptions for such services. Further, there was no  measurable effect on prenatal visits, sonograms, or hospitalization following delivery. While a highly informed and “rational” consumer would not forgo any such services even if facing full cost sharing (because the expected delivery hospitalization essentially caps spending at the deductible level), immediate-term outof- pocket costs might remain important potential barriers to HDHP members, especially among lower-income women.

High-deductible benefit design was associated with increased out-of-pocket costs for maternity care, but had no adverse impacts on receipt of recommended prenatal and postpartum care.

High-Deductible Health Plans and Costs and Utilization of Maternity Care

Transition from an HMO to a high-deductible health plan with deductible exemptions for routine preventive maternity services was associated with:  n Substantial increases in out-of-pocket expenses for maternity and delivery-related care (although information on changes in employer account contributions and employee premium contributions was not available).  n A statistically insignificant (but large) reduction in overall costs.  n No apparent adverse impacts on recommended prenatal and postpartum care.

	Childbirth is the leading reason for hospitalization in the United States.

	Although HDHPs are intended to decrease escalating health costs and discretionary care, they have generated controversy.

	Our study examined this question among women insured by a large Massachusetts-based health insurance program (Harvard Pilgrim Health Care) that predominantly insures members through HMO plans and began offering HDHPs in April 2002. We anticipated that HDHP membership would increase out-of-pocket maternity expenditures, shift costs to members, and possibly reduce total maternity care expenditures, consistent with the cost-containment goal of consumer-directed healthcare.

	Although use of exempted services might be expected to remain stable, we hypothesized 3 mechanisms that could decrease utilization. (1) Cost sharing for nonexempt services (such as laboratory tests) ordered during prenatal visits could discourage subsequent visits. (2) The intricacy of maternity benefit design in HDHPs (eg, deductible exemptions based on clinical conditions) combined with uncertainty regarding costs of anticipated services could cause confusion and reduced care even for exempt services.

	The objective of this study was to evaluate the impact of transition from an HMO to an HDHP on the costs and utilization of maternity and delivery care services within the 6 months before and 3 months following delivery.

	The Office for Sponsored Programs (Harvard Pilgrim Health Care Institute) reviewed this study and granted institutional review board approval.

	We used a pre—post study design with matched control group and a difference-in-differences analysis to measure changes in study outcomes.

	Harvard Pilgrim Health Care is a health plan serving approximately 1 million individuals in New England. On April 1, 2002, Harvard Pilgrim began offering HDHPs with annual deductible amounts of $500 to $2000 for individuals and $1000 to $4000 for families. Members of family plans also had individual deductibles equal to half of the family deductible. Of Harvard Pilgrim’s commercially insured members, approximately 70% have HMO plans, 20% have preferred provider organization (PPO) plans, and 10% have HDHPs, compared with corresponding national rates of 58%, 19%, and 13%.

	Insurance coverage for maternity care in Harvard Pilgrim HMO plans includes full coverage of prenatal and postpartum care, with no cost sharing beyond office visit and  hospitalization copayments. Outpatient visit copayments among HMO members in our study ranged from $5 to $25 (median $15) while hospitalization copayments ranged from $0 to $1000 (median $250).

	In the HDHPs we studied, many maternity care services are exempt from the deductible, having either first dollar coverage or low copayments. First dollar coverage applies to routine prenatal and postpartum visits, fetal ultrasounds (sonograms),  routine urinalysis, Papanicolaou tests, and screenings for sexually transmitted infections. All hospital delivery charges and many outpatient procedures are subject to the deductible, but are covered in full after the deductible has been met. Copayments of $20 apply to non—maternity care outpatient visits, urgent care visits, and specialist consultations. These copayments apply regardless of whether the member has exceeded the deductible spending level. However, the HDHPs have an out-of-pocket maximum (including copayments and deductibles) of $2000 to $4000 for individuals and $4000 to $8000 for families. After a member reaches the out-of-pocket maximum, all services are covered in full. Provider networks for women in HDHPs and HMO plans are identical.

	The HDHPs we studied were not eligible to be paired with health savings accounts (HSAs); all were eligible to be combined with health reimbursement accounts (HRAs). Although we could not account for HRA purchases from other companies, only 3% of the HDHPs we studied had HRAs through Harvard Pilgrim. Nationally, fewer than half of HDHPs with deductibles over $1000 are paired with HSA or HRA accounts,

	From the HDHP and HMO groups, we identified women who had given birth using 

	Study outcomes included costs, quality, and utilization, categorized as delivery related (for care provided during the delivery hospitalization) and maternity related (for all types of maternity care, including delivery-related care). Cost measures included out-of-pocket and total expenditures. Out-of-pocket costs comprised all member expenditures toward copayments and deductibles. Total costs were calculated as the sum of the expenditures by the insurer (payer costs) and out-of-pocket expenditures by the member.

	We calculated maternity and delivery-related costs using a previously established set of diagnosis, procedure, and payment codes that included prenatal and postpartum visits,  obstetrical anesthesia, introduction and repair services, vaginal or caesarean delivery, radiology procedures, pathology and laboratory expenses, and in utero procedures.3 We distinguished delivery services from non—delivery-related maternity care both by the specific service and procedure codes and by the timing of occurrence during the delivery hospitalization. Measures of maternity care quality included 3 measures based on Healthcare Effectiveness Data and Information Set (HEDIS) indicators (early prenatal care, appropriate frequency of prenatal care, and timely postpartum care).25 Early prenatal care was defined as having at least 1 prenatal visit within 42 days of the second trimester. Appropriate frequency of prenatal care comprised at least 7 prenatal visits in the 190 days prior to delivery, as per consensus recommendations.26 We calculated this measure only among women who delivered full-term babies (>37 weeks of gestation) due to lack of information on gestational age at delivery for preterm infants. Timely postpartum care was achieved by having a visit between 21 and 56 days after delivery. General maternity care utilization measures included the total number of prenatal visits and sonograms, and the length of stay for the delivery hospitalization.

	Other variables were age at delivery, month of delivery, type of delivery (vaginal or caesarean), gestational diabetes, high-risk pregnancy, preterm delivery, chronic disease score, socioeconomic status, and employer size (number of employees). Clinical conditions were defined using payment or diagnosis codes. As a measure of comorbidity and  care-seeking behavior, we computed the chronic disease score, which predicts future healthcare spending based on pharmacy claims in the baseline year.27 To develop a measure of socioeconomic status, we linked members’ residential addresses to their 2000 US Census block group and created measures of neighborhood education levels and poverty status based on previously established standards.28 Low socioeconomic status was indicated by living in a census block group with either more than 25% of adults having less than a high school education or more than 10% of households living below the poverty level.

	We used regression models in a difference-in-differences analysis to assess the independent effect of switching to an HDHP on costs, quality, and utilization outcomes, after controlling for the covariates listed above. This analysis used 3 types of regression models: log-linear for costs, logistic for dichotomous quality measures, and linear for utilization measures (number of visits or days). We also conducted several sensitivity analyses to test the stability of results. Analyses excluding socioeconomic status (which is measured at the census block level rather than the individual level) and adjusting for inflation (using the medical component of the Consumer Price Index) had no impact on the direction or significance of the original findings.

	All statistical analyses used 2-tailed tests and a levels of .05, and were conducted using SAS version 9.1 (SAS Institute Inc, Cary, NC).

	Study groups were similar, with no statistically significant differences detected (

	In contrast to overall costs, out-of-pocket spending increased following the transition from an HMO to an HDHP. Average out-of-pocket maternity care expenditures among HDHP members rose from $356 before the HDHP transition to $942 for those who delivered after, compared with nearly constant values ($262 to $282) for women who remained inHMO  plans, a relative increase of 106% (

	Achievement of quality standards for early and frequent prenatal care as well as timely postpartum care did not vary significantly across study groups (Table 2). Nearly all women received early prenatal care, and the majority accessed recommended prenatal and postpartum care. Likewise, maternity care utilization was similar across groups, with an average of 9 to 10 prenatal visits and 7 to 8 sonograms (mode = 4; 25% of women had >10 sonograms) and an average delivery hospitalization stay of about 4 days (3 days for vaginal deliveries; 5 days for caesarean deliveries).

	As expected, pregnancy and delivery complications, high chronic disease scores, and caesarean and preterm deliveries were generally associated with higher costs and utilization (

	This study is the first to characterize the impact of HDHPs on maternity care services. Our findings demonstrate that transitioning from an HMO to an HDHP was associated with increased out-of-pocket costs but stable utilization and quality for maternity and delivery care. Out-of-pocket costs represented a small percentage of total maternity costs for both HMO (4%) and HDHP (11%) members and were in the same range as national averages (6% of total costs).

	An overall reduction in costs associated with better consumer choice is one of the goals of HDHPs and other consumer-driven healthcare models.

	Our findings have important implications for state and federal policy. Recently enacted healthcare reform legislation will establish state-sponsored health insurance exchanges, and policy makers must set minimum coverage criteria for included health plans. Given that previous studies have detected adverse impacts of cost sharing on the poor, further research is needed to investigate how HDHPs affect maternity care among lower-income employed women, who might increasingly be enrolled in HDHPs because of the Patient Protection and Affordable Care Act’s individual mandate for health insurance coverage and taxation of more generous “Cadillac” health plans.

	This study has a number of important limitations. Data on the prevalence of HRAs, employer contributions to such accounts, and employee premium payments were not available, limiting our ability to examine overall cost impacts on healthrelated expenditures. However, as above, we expect a very limited prevalence of HRAs among the small employers included in our cohort. Although it would be more broadly generalizable to include information on PPO members, we were not able to do so in this study. The primary differences compared with PPOs are that HMOs generally have a more limited provider network, lower potential out-of-pocket maternity costs, and higher acn tuarial values.

 These  differences are important when interpreting study results in the national context, and future studies should include PPOs with and without high deductibles. Womenin our study were insured primarily through small employers. HDHP benefit structures and cost-sharing requirements may differ in the small, individual, and large employer group markets.