Original article Access to Oral Osteoporosis Drugs Among Female Medicare Part D Beneficiaries Chia-Wei Lin, MS a , Pinar Karaca-Mandic, PhD b, * , Jeffrey S. McCullough, PhD b , Lesley Weaver, MPP b a Titus Family Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California b Division of Health Policy and Management, University of Minnesota, School of Public Health, Minneapolis, Minnesota Article history: Received 13 June 2013; Received in revised form 7 March 2014; Accepted 1 April 2014 abstract Background: For women living with osteoporosis, high out-of-pocket (OOP) drug costs may prevent drug therapy initiation. We investigate the association between oral osteoporosis OOP medication costs and female Medicare ben- eficiaries’ initiation of osteoporosis drug therapy. Methods: We used 2007 and 2008 administrative claims and enrollment data for a 5% random sample of Medicare beneficiaries. Our study sample included age-qualified, female beneficiaries who had no prior history of osteoporosis but were diagnosed with osteoporosis in 2007 or 2008. Additionally, we only included beneficiaries continuously enrolled in stand-alone prescription drug plans. We excluded beneficiaries who had a chronic condition that was contraindicated with osteoporosis drug utilization. Our final sample included 25,069 beneficiaries. Logistic regression analysis was used to examine the association between the OOP costs and initiation of oral osteoporosis drug therapy during the year of diagnosis. Findings: Twenty-six percent of female Medicare beneficiaries newly diagnosed with osteoporosis initiated oral oste- oporosis drug therapy. Beneficiaries’ OOP costs were not associated with the initiation of drug therapy for osteoporosis. However, there were significant racial disparities in beneficiaries’ initiation of drug therapy. African Americans were 3 percentage points less likely to initiate drug therapy than Whites. In contrast, Asian/Pacific Islander and Hispanic beneficiaries were 8 and 18 percentage points, respectively, more likely to initiate drug therapy than Whites. Addi- tionally, institutionalized beneficiaries were 11 percentage points less likely to initiate drug therapy than other beneficiaries. Conclusions: Access barriers for drug therapy initiation may be driven by factors other than patients’ OOP costs. These results suggest that improved osteoporosis treatment requires a more comprehensive approach that goes beyond payment policies. Copyright Ó 2014 by the Jacobs Institute of Women’s Health. Published by Elsevier Inc. Osteoporosis is an asymptomatic disease characterized by low bone density that increases the risk of experiencing bone frac- tures. Half of all women over the age of 50 will experience an osteoporosis-related bone fracture in their lifetime (National Osteoporosis Foundation, 2002). In a study of the older Medi- care beneficiaries enrolled in fee-for-service Medicare continu- ously for 6 years (Cheng et al., 2009), osteoporosis prevalence was particularly high among women (42.5% vs.10.1% among men). The same study found that prevalence rates also varied by race and ethnicity, with the lowest rate among African Americans (16.5% vs. 38.9% among Asian Americans, 32.4% among Hispanic Americans, 30.7% among Whites). Moreover, 18.4% of presumed Funding Sources: This research was supported by a faculty development grant (AHC-FRD Grant 09.11) from the University of Minnesota Academic Health Center. Dr. Karaca-Mandic also had support from the National Institute on Aging (Grant 5K01AG036740). Funding was used for data purchase and research as- sistant support. The funding organization played no role in the conduct of this study. Ethical approval: The study proposal was submitted to the Institutional Review Board (IRB) at the University of Minnesota reviewed the study. Because all data were de-identified, the study was exempted from human subjects review (proposal # 0912E75303). * Correspondence to: Pinar Karaca-Mandic, PhD, Division of Health Policy and Management University of Minnesota School of Public Health 430 Delaware Street SE, MMC 729 Minneapolis, MN 55455. Phone: 612-624-8953; fax: 612-624-2196. E-mail address: [email protected](P. Karaca-Mandic). www.whijournal.com 1049-3867/$ - see front matter Copyright Ó 2014 by the Jacobs Institute of Women’s Health. Published by Elsevier Inc. http://dx.doi.org/10.1016/j.whi.2014.04.002 Women's Health Issues xxx-xx (2014) e1–e11
Transcript
Women's Health Issues xxx-xx (2014) e1–e11
www.whijournal.com
Original article
Access to Oral Osteoporosis Drugs Among Female MedicarePart D Beneficiaries
Chia-Wei Lin, MS a, Pinar Karaca-Mandic, PhD b,*, Jeffrey S. McCullough, PhD b,Lesley Weaver, MPP b
a Titus Family Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, Schaeffer Center for Health Policy and Economics,University of Southern California, Los Angeles, CaliforniabDivision of Health Policy and Management, University of Minnesota, School of Public Health, Minneapolis, Minnesota
Article history: Received 13 June 2013; Received in revised form 7 March 2014; Accepted 1 April 2014
a b s t r a c t
Background: For women living with osteoporosis, high out-of-pocket
(OOP) drug costs may prevent drug therapyinitiation. We investigate the association between oral osteoporosis OOP medication costs and female Medicare ben-eficiaries’ initiation of osteoporosis drug therapy.Methods: We used 2007 and 2008 administrative claims and enrollment data for a 5% random sample of Medicarebeneficiaries. Our study sample included age-qualified, female beneficiaries who had no prior history of osteoporosisbut were diagnosed with osteoporosis in 2007 or 2008. Additionally, we only included beneficiaries continuouslyenrolled in stand-alone prescription drug plans. We excluded beneficiaries who had a chronic condition that wascontraindicated with osteoporosis drug utilization. Our final sample included 25,069 beneficiaries. Logistic regressionanalysis was used to examine the association between the OOP costs and initiation of oral osteoporosis drug therapyduring the year of diagnosis.Findings: Twenty-six percent of female Medicare beneficiaries newly diagnosed with osteoporosis initiated oral oste-oporosis drug therapy. Beneficiaries’ OOP costs were not associated with the initiation of drug therapy for osteoporosis.However, there were significant racial disparities in beneficiaries’ initiation of drug therapy. African Americans were 3percentage points less likely to initiate drug therapy than Whites. In contrast, Asian/Pacific Islander and Hispanicbeneficiaries were 8 and 18 percentage points, respectively, more likely to initiate drug therapy than Whites. Addi-tionally, institutionalized beneficiaries were 11 percentage points less likely to initiate drug therapy than otherbeneficiaries.Conclusions: Access barriers for drug therapy initiation may be driven by factors other than patients’ OOP costs. Theseresults suggest that improved osteoporosis treatment requires a more comprehensive approach that goes beyondpayment policies.
Copyright � 2014 by the Jacobs Institute of Women’s Health. Published by Elsevier Inc.
Funding Sources: This research was supported by a faculty developmentgrant (AHC-FRD Grant 09.11) from the University of Minnesota Academic HealthCenter. Dr. Karaca-Mandic also had support from the National Institute on Aging(Grant 5K01AG036740). Funding was used for data purchase and research as-sistant support. The funding organization played no role in the conduct of thisstudy.Ethical approval: The study proposal was submitted to the Institutional Review
Board (IRB) at the University of Minnesota reviewed the study. Because all datawere de-identified, the study was exempted from human subjects review(proposal # 0912E75303).* Correspondence to: Pinar Karaca-Mandic, PhD, Division of Health Policy and
Management University of Minnesota School of Public Health 430 DelawareStreet SE, MMC 729 Minneapolis, MN 55455. Phone: 612-624-8953; fax:612-624-2196.
1049-3867/$ - see front matter Copyright � 2014 by the Jacobs Institute of Women’http://dx.doi.org/10.1016/j.whi.2014.04.002
Osteoporosis is an asymptomatic disease characterized by lowbone density that increases the risk of experiencing bone frac-tures. Half of all women over the age of 50 will experience anosteoporosis-related bone fracture in their lifetime (NationalOsteoporosis Foundation, 2002). In a study of the older Medi-care beneficiaries enrolled in fee-for-service Medicare continu-ously for 6 years (Cheng et al., 2009), osteoporosis prevalencewasparticularly high among women (42.5% vs. 10.1% among men).
The same study found that prevalence rates also varied byrace and ethnicity, with the lowest rate among African Americans(16.5% vs. 38.9% among Asian Americans, 32.4% among HispanicAmericans, 30.7% among Whites). Moreover, 18.4% of presumed
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osteoporosis cases had fracture-related claims. The prevalence of“fracture-only” cases was highest among African Americans(18.6% vs. 6.2% among Asian Americans, 12% among HispanicAmericans, 14.3% among Whites). The low proportion of non-fracture osteoporosis diagnosis codes among African Americanssuggests important racial disparities in osteoporosis detectionand prevention (Cheng et al., 2009). Similarly, Hamrick, Cao,Agbafe-Mosley, and Cummings (2012) examined racial dispar-ities in primary care physicians’ screening for and treatment ofosteoporosis. Among women diagnosed with osteoporosis,African Americans were less likely to receive bone densityscreening referrals and prescription medications than Whites.
Womenwith osteoporosis can reduce the risk of bone fracturesby taking prescription osteoporosis drugs, which can be a cost-effective means of reducing fractures (Borgstr€om & Kanis, 2008;Hagen et al., 2011; King, Saag, Burge, Pisu, & Goel, 2005; Pfisteret al., 2006; Qaseem et al., 2008; Tosteson, Burge, Marshall, &Lindsay, 2008; Zethraeus, Borgstr€om, Str€om, Kanis, & J€onsson,2007). According to treatment guidelines and the recommenda-tion from National Osteoporosis Foundation, patients newlydiagnosed with osteoporosis should receive pharmacologicalosteoporosis treatments. Oral osteoporosis drugs are consideredthe first-line therapy for those without contraindications andserious allergic reaction (National Osteoporosis Foundation,2013). Other studies focus on the roles of compliance and adher-ence in osteoporosis treatment (Brookhart et al., 2007; McCombs,Thiebaud, McLaughlin-Miley, & Shi, 2004; Solomon et al., 2005;Weycker, Macarios, Edelsberg, & Oster, 2006).
Despite the benefits of osteoporosis medications, somepatients with osteoporosis do not receive osteoporosis medica-tion therapy. Among patients who start medication therapy, lowadherence rates and high discontinuation rates are common(Brookhart et al., 2007; Solomon et al., 2005; Weycker et al.,2006). In general, higher pharmacy cost sharing is associatedwith the use of fewer medications, especially among older adults(Harris, Stergachis, & Reid,1990; Smith,1993; Lillard, Rogowski, &Kington, 1999; Joyce, Escarce, Solomon, & Goldman, 2002;Goldman et al., 2004; Goldman, Joyce, & Zheng, 2007; Karaca-Mandic, Swenson, Abraham, & Kane, 2012). Although MedicarePart D has increased beneficiaries’ access to prescription drugs(Licthenberg & Sun, 2007), there are significant differences indrug plans’ cost-sharing (Karaca-Mandic, Swenson, et al., 2012),tiered formulary structures (Hoadley, Hargrave, Merrell,Cubanski, & Neuman, 2007), number of covered drugs (Hoadley,Hargrave, Merrell, Cubanski, & Neuman, 2008), and provision ofgap coverage (Hoadley, Cubanski, Hargrave, Summer, & Neuman,2009). Concerns have also been raised about the adverse effect ofthe donut hole onmedication use (Zhang, Donohue, Newhouse, &Lave, 2009; Raebel, Delate, Ellis, & Bayliss, 2008; Fung et al., 2010;Hsu et al., 2008; Gu, Zeng, Patel, & Tripoli, 2010; Hales & George,2010). Conwell and colleagues (2011) found that, once benefi-ciaries with partial or no gap coverage reached the gap, theywere more likely to discontinue osteoporosis medication usethan beneficiaries with full gap coverage because of increasedout-of-pocket (OOP) costs.
However, the role of OOP medication costs in deterring drugtherapy initiation amongMedicare Part D beneficiaries is notwellunderstood. This is particularly important, because the U.S.Medicare program is the primary source of health care insurancefor Americans over the age of 65 and for individuals with specificdisabilities and conditions such as end-stage renal diseaserequiring hemodialysis. Medicare covers the costs of inpatientcare through Part A, the costs of outpatient care through Part B,
and the costs of prescription drugs through Medicare Part D.Although the majority of Medicare enrollees receive their insur-ance through traditional fee-for-service Medicaredwhere pro-viders bill the Centers for Medicare and Medicaid Services (CMS)directly for any care provideddapproximately 25% of Medicareenrollees receive their insurance through Medicare Advantageinsurance (otherwise referred to as Part C). Furthermore, Medi-care and Medicaid (a means-tested health insurance program)provide a series of subsidies for low-income beneficiaries.
In this study, we examined how plan OOP costs were asso-ciated with osteoporosis drug therapy initiation among aged-qualified female Medicare Part D enrollees in stand-aloneprescription drug plans. We also investigated racial and socio-economic disparities in therapy initiation.
Methods
Data
We employed the Prescription Drug Event (PDE) and enroll-ment data for the 5% Medicare random sample from 2006to 2008. These data were combined with the Medispan DrugDatabase. These data were used to identify prescriptions corre-sponding to National Drug Codes for oral osteoporosis medica-tions (alendronate, ibandronate, risedronate, and raloxifene) andto measure oral osteoporosis drug initiation. We also used the2005–2008 Medicare Provider Analysis and Review (MedPAR) filesand the Chronic Condition Data Warehouse (CCW) Chronic Con-ditions Summary files to identify osteoporosis diagnosis andrelated comorbidities. These data were merged with the Benefi-ciary Summary Files, which contains beneficiaries’ demographicinformation. The Plan Characteristics Files that describe costsharing information by tier type of each plan were used toconstruct a measure of plan generosity. Appendix Table 1 pro-vides a summary of all data files and relevant variables used inthe analysis.
Study Sample
We constructed two distinct cohorts of age-qualified, femaleMedicare beneficiaries newly diagnosed with osteoporosis dur-ing the calendar years of 2007 and 2008, respectively. Osteopo-rosis diagnoses were identified by relevant flags in the CCWChronic Conditions Summary files and by primary InternationalClassification of Diseases, Ninth Revision inpatient diagnosiscodes of 733.00, 733.01, 733.02, and 733.09 in MedPAR files.
To identify new osteoporosis diagnoses with a sufficientperiod of prior history, we only included women enrolled intraditional fee-for-service Medicare since 2005, or since turningage 65, whichever was earlier. The sample was also restricted towomen continuously enrolled in a stand-alone prescription drugplans during the cohort calendar year and at least 6 monthsbefore the calendar year because CCW Chronic Conditions Sum-mary files and MedPAR files are not available for beneficiariesenrolled in Medicare Advantage plans.
We constructed each cohort by including womenwho had noprior osteoporosis diagnosis history and no utilization of oralosteoporosis drugs utilization in the PDE files before the studycohort year, and had an osteoporosis chronic condition first timein the study cohort year.
We also excluded women who had chronic conditions thatprohibited oral osteoporosis drug utilization, such as those withend-stage renal disease and hypercalcemia. Furthermore, we
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excluded women with chronic conditions that are often treatedwith osteoporosis drugs, such as Paget’s disease of the bone(Halpern et al., 2011), malignant cancer, steroid-induced osteo-porosis, bone-related cancers (Brandi, 2010; Halpern et al., 2011),and osteogenesis imperfecta (Rosen, 2013). Among the 5%Medicare random sample, 100% of the women who met theseinclusion criteria were included in our sample. Our final studysample included 25,069 women.
Measures
Oral osteoporosis drug initiation was defined as observingat least one prescription for a drug containing the activeingredients alendronate, ibandronate, risedronate, or raloxifeneduring the cohort year. These active ingredients constitute thefirst-line therapy for treatment of osteoporosis (Rosen, 2013).
Consistent with prior research (Goldman et al., 2004; Karaca-Mandic, Jena et al., 2012; Karaca-Mandic, Joyce, Goldman, &Laouri, 2010; Karaca-Mandic, Swenson, et al., 2012), plan OOPdrug costs were computed for each beneficiary not receiving anylow-income subsidy (LIS) by calculating the average monthlyOOP cost for a representative, fixed basket of oral osteoporosisdrugs separately for 2007 and 2008 (Karaca-Mandic, Jena et al.,2012; Karaca-Mandic et al., 2010; Karaca-Mandic, Swensonet al., 2012). The Part D Denominator Files, Prescription Drug EventFiles and Plan Characteristics Files allowed us to identify benefi-ciaries’ enrollment in specific drug plans, their initiation of oralosteoporosis medications, and their plan’s cost-sharing infor-mation during the pre-initial coverage limit (pre-ICL) and donut(gap) phases, respectively. Our summary measure captures theplan’s OOP costs both in the pre-ICL and gap phases independentof any individual beneficiary’s oral osteoporosis drug choice orutilization.
We focused on the OOP costs for the standardized basket oforal osteoporosis drugs for each plan rather than average OOPcosts by individual beneficiary because the latter would reflect abeneficiary’s preferences regarding lower versus higher costmedications given his or her particular plan design, leading tomisleading plan generosity comparisons. For example, considertwo plans: Plan A covers both drugs 1 and 2 with a copayment of$30, whereas plan B covers drug 1 with copayment of $30 anddrug 2 with a copayment of $60. If most patients choose thecheaper drug in plan B, there is little difference observed in theaverage OOP that beneficiaries pay in the two plans. However, acomparison of the benefit designs suggests otherwise; plan A hasmore generous coverage.
For each active ingredient, we first examined the share of30-day equivalent prescriptions dispensed under the followingtier types for the overall study sample separately for 2007 and2008: 1) brand/preferred brand, 2) non-preferred brand, 3)generic/preferred generic, and 4) non-preferred generic. Second,using information on each plan’s copayment information forthese tier types, we estimated the expected average OOP cost ofeach active ingredient in the plan using the shares by tier type(based on use in the full sample as weights). Therefore, utiliza-tionweights used to construct the representative basket for eachphase varied only by year, not by plan or by beneficiary. This isimportant because plan- or beneficiary-level utilization of theactive ingredients would be endogenous. In particular, patientresponses to OOP prices of active ingredients in the plan wouldalter the composition of medications in each plan. The fixedbasket of medications across individuals allows for a comparisonof the OOP costs across different plans. For example, consider an
active ingredient that is dispensed under preferred brand tier95% and non-preferred brand tier 5% across all users. A planwithcopayments of $30 and $60 for preferred and non-preferredbrand tiers, respectively, would have average monthly OOP of$32 (0.95 � 30 þ 0.05 � 60) for this active ingredient. We con-ducted these estimations separately for the pre-ICL and gapcoverage phases and estimated the annual OOP cost as weightedthe average OOP cost by weighting the monthly OOP in the pre-ICL and gap coverage phases by the average number of monthsbeneficiaries’ spent in each phase.
The OOP costs of Medicare–Medicaid dual eligible womenand recipients of LIS were based on their copayments. Thesecopayments were determined by the CMS depending on bene-ficiary income (United States General Accountability Office,2007; Medpac, 2008). There are three income categoriesamong the dual eligible beneficiaries in general. Income category1 included “full benefit institutionalized dual eligible benefi-ciaries”who were “deemed eligible” irrespective of their incomeand assets. They had no copayments. The second income cate-gory included “full benefit, non-institutionalized, dual-eligiblebeneficiaries” who were also “deemed eligible” based on in-comes below 100% of the federal poverty level (FPL). They hadsmall fixed monthly copayments (approximately $1 for genericand $3 for branded drugs). The third income category included“full-benefit, dual-eligible beneficiaries”with incomes over 100%of the FPL and “partial benefit, dual-eligible beneficiaries,” whowere also “deemed eligible” because they receive premium andcost-sharing assistance from Medicaid. These beneficiariesreceive financial assistance through the Medicare Savings Pro-grams and constitute beneficiary categories such as the QualifiedMedicare Beneficiary, Specified Low-Income Medicare Benefi-ciary, and Qualifying Individual. They also faced small, fixed,monthly copayments (approximately $2 for generic and $5 forbranded drugs). The fourth income category included benefi-ciaries who were not dual eligible, but received LIS because theirincome fell below 135% of the FPL and their assets were lowerthan $7,790 for an individual or $12,440 for a couple. They alsofaced a fixed monthly copayment of $2 for generics and $5 forbrands. We grouped all other LIS recipient beneficiaries into in-come category 5. They had income below 150% of poverty limitand assets below $11,990 for an individual and $23,970 for acouple. They faced a fixed coinsurance rate of 15% for all pre-scription drugs. Finally, income category 6 included beneficiariesnot receiving LIS (highest income group).
Beneficiary Characteristics
We controlled for numerous demographic characteristics,including age, race/ethnicity, and income categories (incomecategories 1–3 for Medicare–Medicaid dual eligible benefi-ciaries; categories 4 and 5 for LIS recipients who are not dualeligible; and category 6 for beneficiaries not receiving any LIS).Because beneficiaries can switch income category frommonth tomonth during the year, we classified beneficiaries based on thecategory they were most often enrolled in during the year. Wealso controlled for the number of category transitions within ayear to capture income stability. Controlling for income categorytransitions was important, because this is a source of OOP pricevariation.
We classified women who exceeded the pre-ICL phase andreached the gap during the previous year as having ‘high overallmedication cost,’ and controlled for this in ourmodels. To controlfor their consumption of other (i.e., non-osteoporosis) drugs, we
Table 1Characteristics of Female Medicare Beneficiaries with Osteoporosis
Variable Oral Osteoporosis DrugTherapy
p-Value
Initiators Non-initiators
Demographic characteristicsAge, mean (SD) 77.25 (0.06) 75.80 (0.09) <.001Race/ethnicity, n (%)Non-Hispanic White 4,960 (24.35) 15,413 (75.65) <.001African American 408 (23.31) 1,342 (76.69) .008Asian/Pacific Islander 435 (48.17) 468 (51.83) <.001Hispanic 626 (35.92) 1,117 (64.08) <.001Other/unknown race 94 (30.72) 212 (69.28) .059
* Income category 1, full benefit “deemed” dual eligible, institutionalized;income category 2, full benefit “deemed” dual eligible based on incomes <100%federal poverty level (FPL), non-institutionalized; income category 3, full benefitdual eligible based on incomes >100% FPL and partial benefit “deemed” dual
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constructed several binary indicators that identified whether ornot a beneficiary used prescription drugs from other therapeuticclasses during the cohort year. Similarly, we constructed binaryindicators for whether or not the beneficiary had cataracts,congestive heart failure, diabetes, ischemic heart disease, andrheumatoid or osteoarthritis by using the CCWChronic ConditionsSummary files during the cohort year. Additionally, we controlledfor other characteristics of the beneficiary’s drug plan, includingthe presence of gap coverage, deductible amount, and indicatorsfor prescription drug plan regions. We also controlled for zip-code–level median household income based on beneficiaryresidence.
Statistical Analysis
We estimated several logistic regression models to identifyfactors associated with oral osteoporosis drug initiation. Model 1predicted drug initiation as a function of the OOP cost for a fixedbasket of osteoporosis drugs and beneficiaries’ demographiccharacteristics, namely race, ethnicity, age, and income category.Model 2 incorporated risk adjusters for beneficiaries’ healthstatus. Risk adjustors included controls for beneficiaries’comorbidities and utilization of non-osteoporosis pharmaceuti-cals. Models 3 through 5 allowed price sensitivity to vary acrosssubpopulations. These models included all Model 2 covariates,and added interactions between OOP costs and patient charac-teristics. Model 3 allowed price sensitivity to vary across incomecategories,1 whereas Models 4 and 5 allowed price sensitivity todiffer by race/ethnicity and health status, respectively.
In a sensitivity analysis, we constructed a plan OOP measurethat varied by whether or not the beneficiary expected to reachthe gap. On average, beneficiaries who never exceeded the pre-ICL period spent 12 months in that phase, and those whoreached the gap phase spent 7 months in the pre-ICL and5 months in the gap phases, respectively. For beneficiaries whoreached the gap in the previous yeardthe “high medication costgroup” (our best estimate for beneficiaries who were likely toreach the gap in the current year as well)dwe constructed theOOP cost measures as the weighted average of the monthly OOPin pre-ICL and gap phases using 7 and 5 as the weights. Forbeneficiaries who did not reach the gap in the previous yeardthe“low medication cost group”dwe used 12 and 0 as weights.
In two other sensitivity analyses, we restricted our studysamples to include only women with at least a 3-month periodand a 6-month period after diagnosis, respectively. The outcomevariables in these specifications were defined as initiation of oralosteoporosis therapy during either the 3- or 6-month follow-upperiod.
We report the marginal effects, the change in probability oftherapy initiation from a unit change for any given explanatoryvariable, for each of our models. Statistical analyses were per-formed using STATA 12 (StataCorp, 2011).
Results
Table 1 summarizes the demographic characteristics,comorbidities, and drug utilization of beneficiaries who did anddid not initiate oral osteoporosis drug therapy. Beneficiaries who
1 Note that the LIS program fixes prices for those with the lowest incomes. Forthis population, the price sensitivity parameter is identified by variation as eligi-bility shifts across categories conditional on the number of income categorytransitions. Results were consistent across a number of alternative specifications.
initiated drug therapy tended to be slightly older than those whodid not initiate drug therapy (77.25 vs. 75.80 years). Non-Hispanic White women had an higher initiation rate relative toAfrican-American women (24.35% vs. 23.31%). However, relative
eligible; income category 4, not dual eligible, but receives low-income subsidybecause income <135% FPL, assets < $7,790 (individual)/$12,440 (couple);income category 5, Not dual eligible, but receives low-income subsidy becauseincome <150%FPL, assets < $11,990 (individual)/$23,970 (couple); incomecategory 6, does not receive low-income subsidy.
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to non-Hispanic White women (24.35%), Asian/Pacific Islander(48.17%) and Hispanic (35.92%) women had an higher initiationrate. A smaller share of institutionalized women initiated drugtherapy compared with all other beneficiaries. For example,12.45% of beneficiaries in income category 1 initiated drugtherapy compared with 29.92% and 24.57% of beneficiaries inincome categories 5 and 6, respectively. Approximately 23% ofbeneficiaries with diabetes, ischemic heart disease, or rheuma-toid/osteoarthritis initiated oral osteoporosis drug therapy.
Table 2 displays the marginal effects of patient characteristicson oral osteoporosis drug therapy initiation based on multi-variate logistic regression model estimates. Model 1 resultsdemonstrate that women in income category 1 were 12% lesslikely to initiate than women in income category 6. The numberof income category switches did not have an associationwith theprobability of initiation. Furthermore, African-American womenwere 3% less likely to initiate drug therapy than non-HispanicWhite women. In contrast, Asian/Pacific Islander and Hispanicwomen were 18% and 7% more likely than non-Hispanic Whitewomen to initiate drug therapy, respectively. Women with highoverall medication cost status (who reached gap phase in pre-vious year) were 2% to 3% less likely to initiate oral osteoporosismedication. Finally, OOP costs have no discernible effect on drugtherapy initiationwhenwe controlled for observable patient andplan characteristics.
Model 2 controlled for beneficiaries’ comorbidities as well astheir drug utilization for conditions other than osteoporosis.
Table 2Marginal Effects for Patients’ Sociodemographic Characteristics on Probability of Initi
Variable Model 1y
dy/dx
Out-of-pocket cost (in $100s of dollars) �0.006Deductible (in $100s of dollars) �0.002Gap coverage �0.003Income categoryx
No. of income category changes during the year �0.012Non-Hispanic White ReferenceAfrican American �0.026*Asian/Pacific Islander 0.177***Hispanic 0.069***Other/unknown race 0.040High overall medication cost status �0.026***Age �0.003***Number of 30 day equivalent non-osteoporosis medicationsCataractsCongestive heart failureDiabetesIschemic heart diseaseRheumatoid/osteoarthritisSample size 25,069
Abbreviation: dy/dx, change in probability of initiation (y) from a one-unit increase i*p < .05, **p < .01, ***p < .001.
y Model 1 controlled for cohort year, median family income, and indicators for prez Model 2 controlled for cohort year, median family income, indicators for prescrip
Table 1, and comorbidities.x Income category 1, full benefit “deemed” dual eligible, institutionalized; income
poverty level (FPL), non-institutionalized; income category 3, full benefit dual eligiblecategory 4, not dual eligible, but receives low-income subsidy because income <135%eligible, but receives low-income subsidy because income <150% FPL, assets < $11,99subsidy.
Results were similar to Model 1 estimates for income category1, African Americans, Asian/Pacific Islander, and Hispanics.Comorbidities also influence their likelihood of drug initiation.Womenwith congestive heart failure, diabetes, and rheumatoid/osteoarthritis were 3% less likely than women without theseconditions to initiate oral osteoporosis drug therapy. Comparedwith women without cataracts and ischemic heart disease,women with these conditions were 1% and 2% less likely toinitiate drug therapy. High overallmedication cost statuswas alsonegatively associated with probability of initiation. Interestingly,womenwho consume a larger number of non-osteoporosis drugsin the cohort year were more likely to initiate oral osteoporosistherapy, likely reflecting preferences for medication therapy ingeneral.
Table 3 displays marginal effects from Models 3 through 5.These models interact beneficiaries’ OOP drug costs with incomecategory, race, and health status, respectively, to allow for pricesensitivity to vary across these characteristics. The marginaleffects reported in Table 3 describe the association of OOP priceon subpopulations by income category (Model 3), race/ethnicity(Model 4), and health status (Model 5). Model 3 results suggestthat women in lower income categories were no more pricesensitive than those with higher incomes categories. Althoughthe estimated effects are not significantly different from zero,they are sufficiently precise to rule out a 1% decrease in proba-bility of initiation from a one standard deviation OOP priceincrease for all income categories. This implies that we precisely
scription drug plan region.tion drug plan region, indicators for utilization of each other drug class listed in
category 2, full benefit “deemed” dual eligible based on incomes <100% federalbased on incomes >100% FPL and partial benefit “deemed” dual eligible; incomeFPL, assets < $7,790 (individual)/$12,440 (couple); income category 5, not dual
0 (individual)/$23,970 (couple), income category 6, does not receive low-income
Table 3Marginal Effects of Out-of-Pocket Costs on Probability of Initiation by Income, Race, and Health Status Categories
Abbreviation: dx/dy, change in probability of initiation (y) from a one-unit increase in the explanatory variable (x).*p < .05, **p < .01, ***p < .001.All models controlled for the deductible amount, gap coverage, number of income category changes during the year, high overall medication cost status, race, age, cohortyear, comorbidities, number of 30 day equivalent non-osteoporosis medications used during the cohort year, indicators for utilization of each other drug class listed inTable 1, median family income and indicators for prescription drug plan regions.
y Income category 1, full benefit “deemed” dual eligible, institutionalized; income category 2, full benefit “deemed” dual eligible based on incomes <100% federalpoverty level (FPL), non-institutionalized; income category 3, full benefit dual eligible based on incomes >100% FPL and partial benefit “deemed” dual eligible; incomecategory 4, not dual eligible, but receives low-income subsidy because income <135% FPL, assets < $7,790 (individual)/$12,440 (couple); income category 5, not dualeligible, but receives low-income subsidy because income <150%FPL, assets < $11,990 (individual)/$23,970 (couple); income category 6, Does not receive low-incomesubsidy.
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estimated parameters that are quite close to zero. As an example,consider income category 2. The estimated marginal effect is-0.06, and the lower bound of the confidence interval reported inTable 3 (Model 3) suggests we are 95% certain that the largestdecrease in the probability of initiation is 23%. Multiplying -23%with 1 standard deviation of OOP in the sample (0.015), weestimate a reduction of 0.35%, a less than 1 percentage pointdecrease in the probability of initiation. In Model 4, we observethat African Americans were not notably more price sensitivethan other beneficiaries. Asian/Pacific Islander and Hispanicwomen were, however, slightly more price sensitive than non-Hispanic White women. A $100 increase in average annualOOP costs would decrease Asian/Pacific Islander and Hispanicinitiation by approximately 3%. Finally, price sensitivity did notdepend on patients’ comorbidities in Model 5. For each comor-bidity, a one standard deviation increase in OOP prices woulddecrease initiation by less than 1%. These results provide strongevidence that the initiation differences described in Table 2 arenot driven by differential price sensitivity across beneficiarysubpopulations.
Table 4 presents a sensitivity analysis by differentiating theplan OOP of women based on their spending in the previousyear: Low medication cost (those who did not reach the gap inprevious year) and high medication cost (those who reached thegap in previous year). Our results are largely comparable withthose in Table 2, Model 2. Although this approach allows forvarying plan OOP by whether the beneficiary expects to reachthe gap or not, it suffers from an important potential bias.Unobserved beneficiary characteristics that influence expecta-tions to reach the gap would also be correlated with our OOPmeasure. Our original approach of using fixed weights across allbeneficiaries for plan OOP for the pre-ICL phase and gap phasesavoids this bias.
Table 5 presents two subsample analyses restricting our studysamples to include only women with at least a 3-month follow-up period and at least a 6-month follow-up period after diag-nosis, respectively. Again, our results are largely similar forinitiation of oral osteoporosis therapy during the 3- and 6-monthfollow-up periods.
Discussion
Our study confirms prior literature on racial and socioeco-nomic disparities in osteoporosis treatment. One of the uniquefeatures of our study is that these findings are confirmed in theparticular context of oral osteoporosis therapy initiation. Moreimportant, our study contributes by providing estimates of racialand socioeconomic barriers for a generalizable, nationally repre-sentative cohort of older American women covered by Medicare.
Among female Medicare beneficiaries, we found racial andsocioeconomic disparities in oral osteoporosis drug therapyinitiation. Compared with non-Hispanic Whites, African Ameri-cans were less likely to initiate drug therapy after controlling forcomorbidities and other demographic characteristics. Further-more, lower initiation among African-American women was notrelated to responsiveness to OOP costs. This finding is consistentwith results from prior research providing evidence of racial andethnic disparities in osteoporosis prevention and detection(Hamrick et al., 2012; Mudano et al., 2003). In their study ofosteoporosis in postmenopausal women, Hamrick and associates(2012) found significant racial disparities in primary carephysicians’ propensity to refer women for bone density screen-ings. Compared with White women, African-American womenwere less likely to be referred for a bone density screening.White women were more likely to receive medication thanAfrican-American women. Their findings suggest that racial
Abbreviation: dy/dx, change in probability of initiation (y) from a one-unit in-crease in the explanatory variable (x).*p < .05, **p < .01, ***p < .001.This specification controlled for cohort year, median family income, indicatorsfor prescription drug plan region, indicators for utilization of each other drugclass listed in Table 1, and comorbidities. The model is the same as Model 2 inTable 2 except for the definition of out-of-pocket (OOP) costs. In both tables, OOPis constructed by weighted the monthly OOP in the pre-ICL and gap coveragephases by the average number of months beneficiaries spent in each phase. InTable 2, these weights are extracted from the full sample, and are the same for allbeneficiaries. In this Table, the weights are determined separately for high andlow cost medication users.
y Income category 1, full benefit “deemed” dual eligible, institutionalized;income category 2, full benefit “deemed” dual eligible based on incomes <100%federal poverty level (FPL), non-institutionalized; income category 3, full benefitdual eligible based on incomes >100% FPL and partial benefit “deemed” dualeligible; income category 4, not dual eligible, but receives low-income subsidybecause income <135% FPL, assets < $7,790 (individual)/$12,440 (couple);income category 5, not dual eligible, but receives low-income subsidy becauseincome <150%FPL, assets < $11,990 (individual)/$23,970 (couple); incomecategory 6, Does not receive low-income subsidy.
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discrimination may be limiting African-Americans’ access toboth bone density screenings and oral osteoporosis drugs.African-American women’s restricted access could adverselyimpact their health status and quality of life. Without access toprescription drugs, African-American women with osteoporosisare at an increased risk of sustaining bone fractures. Comparedwith White women, African-American women who sustain abone fracture experience longer hospital stays and have greatermorbidity and mortality (Furstenberg & Mezey, 1987; Jacobsenet al., 1992). Combined findings of previous studies and ourstudy suggests that improving the utilization of osteoporosismedication among African Americans is likely to have greatimpact on osteoporosis management and fracture prevention.
Dual eligibleMedicare Part D beneficiaries in income category1 (full dual eligible and institutionalized) are less likely to initiateoral osteoporosis drug therapy even though they face no OOPdrug costs. This suggests that they face other significant access
barriers. Future research could investigate in detail potentialaccess barriers related to being institutionalized. Becausewomen in poverty face above-average osteoporosis and bonefractures prevalence (Navarro et al., 2009), medical providersand policy makers should increase osteoporosis medication ac-cess within institutionalized population.
High overall medication costs, as indicated by spending intothe gap phase during the prior year, were associatedwith a lesserlikelihood of initiating oral osteoporosis drug therapy. This resultis consistent with prior research, which demonstrates thatbeneficiaries falling into the Medicare coverage gap decreasedmedication utilization (Fung et al., 2010; Gu et al., 2010; Hales &George, 2010; Hsu et al., 2008; Raebel et al., 2008; Zhang et al.,2009). Patients tend to reduce “non-essential” drug use ratherthe “essential” drug use when the financial burden increases as aresult of hitting the coverage gap (Goldman et al., 2007). Elderlywomen newly diagnosed with osteoporosis may consider oste-oporosis therapy less essential if they already have high drugcosts owing to other comorbidities. This is because early osteo-porosis is largely asymptomatic, and not considered life threat-ening. It is also possible that although OOP costs are notassociated with drug therapy initiation, they are associated withtherapy adherence and continuation among those who initiate,which we did not investigate in this paper.
Our study has a number of notable limitations. First, wewere unable to exclude beneficiaries who received alternativetherapies because of inadequate swallowing function, being bed-ridden. This could be a potential underlying cause of low initia-tion rate among institutionalized beneficiaries. Second, the PDEdata do not capture the use of alternative treatments such asinjectable osteoporosis drugs. Clinical guidelines suggest thatinjectable ibandronate or zoledronate should be consideredwhen the oral bisphosphates are contraindicated, such ascompromised gastrointestinal absorption, or ineffective, such assignificantly decreasing in bone mineral density (Rosen, 2013).Another treatment, teriparatide injection, is more expensivethan other drugs and tends to be prescribed as sequential ther-apy with bisphosphates (Conwell et al., 2011; Rosen, 2013).Because our study sample includes only women newly diag-nosed with osteoporosis, it is likely that physicians will initiallyprescribe the first-line oral osteoporosis medications we studyrather than the injectable medications. Third, although claimsdata document the filling of prescribed medications, they do notcapture prescriptions that are written but not filled. Althoughthis data limitation should not bias the results presented in themanuscript, we cannot test whether changes in treatmentinitiation are driven by changes in patient or physician behavior.Fourth, there are well-known limitations for measuring race andethnicity from claims data. We use the Research Triangle Insti-tute race codes inMedicare enrollment files to construct race andethnicity categories. Zaslavsky, Ayanian, and Zaborski (2012)found that the Hispanics, Asian, Pacific Islanders, and AmericanIndians were under-identified (sensitivity, 41%, 60%, 39%, and57%, respectively) in Medicare enrollment files, whereas Whitesand African Americans were highly identified (sensitivity, 99%and 98%, respectively). Those misclassified beneficiaries werecategorized as White because it was the largest category(Eicheldinger & Bonito, 2008).
Fifth, unobserved patient characteristics may influencebeneficiaries’ plan selection (e.g., choosing plans with moregenerous coverage) and their decision to initiate therapy,resulting in selection bias. Beneficiaries will, in general, choosedrug plans based on current health care needs and preferences.
Table 5Sensitivity Analyses of Initiation Within 3 and 6 Months of the Osteoporosis Diagnosis, Marginal Effects
Variable Initiation Within 3 Months of Diagnosis Initiation Within 6 Months of Diagnosis
dy/dx 95% CI dy/dx 95% CI
Out-of-pocket cost (in $100s of dollars) �0.001 (�0.017, 0.014) �0.012 (�0.032, 0.008)Deductible (in $100s of dollars) �0.003 (�0.011, 0.005) �0.006 (�0.015, 0.004)Gap coverage �0.004 (�0.029, 0.021) �0.015 (�0.046, 0.016)Income categoryy
Notes:*p < .05, **p < .01, ***p < .001.Both specifications controlled for cohort year, median family income, indicators for prescription drug plan region, indicators for utilization of each other drug class listedin Table 1, and comorbidities. The models are the same as Model 2 in Table 2 except for the definition of study sample and the initiation outcome. The models presentedin Table 2 includes all women diagnosed with osteoporosis in the cohort year while the study samples for the two models presented in this table include only womenwith at least a 3-month follow-up period and a 6-month period after diagnosis, respectively. Although the outcome variable in Table 2 is initiation of oral osteoporosistherapy during the cohort year, the outcome variables in these tables are defined as initiation of oral osteoporosis therapy during the 3- and 6-month follow-up periodcorrespondingly.
y Income category 1, full benefit “deemed” dual eligible, institutionalized; income category 2, full benefit “deemed” dual eligible based on incomes <100% federalpoverty level (FPL), non-institutionalized; income category 3, full benefit dual eligible based on incomes >100% FPL and partial benefit “deemed” dual eligible; incomecategory 4, not dual eligible, but receives low-income subsidy because income <135% FPL, assets < $7,790 (individual)/$12,440 (couple); income category 5, not dualeligible, but receives low-income subsidy because income <150%FPL, assets < $11,990 (individual)/$23,970 (couple); income category 6, Does not receive low-incomesubsidy.
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Because we examined newly diagnosed beneficiaries, planselection decisions are likely independent of the diagnosisbecause these selection decisions were made during November/December of the previous year. In addition, we included a largeset of risk adjusters, such as binary indicators, that identifiedwhether or not a beneficiary use prescription drugs from othertherapeutic classes as well as binary indicators for whether ornot the beneficiary had various comorbidities. Our parametersof interest were not affected by including these controls.Although this suggests that our results do not suffer from se-lection bias, we cannot rule out the possibility that other un-observed factors may be correlated with both initiation andplan selection.
Implications for Practice and/or Policy
Our study found evidence of substantial disparities for oste-oporosis drug therapy initiation among vulnerable populations.In particular, we found that institutionalized women, AfricanAmericans, and patients with major comorbidities were signifi-cantly less likely to initiate osteoporosis treatment. Furthermore,initiation was lower among women with higher overall medi-cation costs, as indicated by spending into the gap phase duringthe prior year. These findings are consistent with the broaderliterature on osteoporosis treatment disparities. Furthermore,
we rejected the hypothesis that these differences were driven byheightened price sensitivity among populations facing accessbarriersdthese access barriers are beyond purely financialconsiderations.
The Affordable Care Act includes provisions that will reducedrug costs for Medicare beneficiaries and provide more supportin the coverage gap (AARP Public Policy Institute, 2010; U.S.Department of Health and Human Services, 2012). Althoughimproved gap coverage will likely improve initiation amongthose with high overall medication costs, these benefits may dolittle to mitigate access barriers faced by many beneficiaries. Inparticular, LIS beneficiaries (including those who are dualMedicare and Medicaid eligible) are insulated from the coveragegap under existing policy (CMS, 2011). Furthermore, the insti-tutionalized dual eligible beneficiaries face no copayments andexperience the lowest initiation rates. Finally, African-Americanpatients and those with major comorbidities were no moreprice sensitive than other populations.
These results suggest that improved osteoporosis treatmentrequires a more comprehensive approach that goes beyond pay-ment policies. Because osteoporosis is often initially asymptom-atic, vulnerable patient populations may benefit from improvededucational initiatives. Prior research suggests that simplifieddrug dosing regimens and electronic prescriptions and refills mayimprove patients’ access to osteoporosis drugs (Hiligsmann et al.,
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2013). These alternatives should be explored and emphasized forhigh-risk populations with low treatment rates.
Finally, we note that although the Affordable Care Actmay notaddress some barriers to initiation, it may have other benefitsthat we do not explore. Improved coverage will reduce thefinancial risk and burden of disease among the elderly. Improvedgap coverage will likely have a greater effect on continuation ofdrug therapy than initiation.
Acknowledgments
This work was carried out in part using computing resourcesat the University of Minnesota Supercomputing Institute.
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Author Descriptions
Chia-Wei Lin, MS, is a PhD student in the pharmaceutical economics and policyprogram at the University of Southern California. Her research focuses on theeconomic and health outcomes evaluation for pharmaceutical products.
Pinar Karaca-Mandic, PhD, is an assistant professor in the Division of Health Policyand Management at the University of Minnesota, School of Public Health, andfaculty research fellow at the National Bureau of Economic Research. Her researchfocuses on health insurance benefit design, health insurance market, and healthcare policy and regulations.
Jeffery McCullough, PhD, is an assistant professor in the Division of Health Policyand Management at the University of Minnesota, School of Public Health. Hisresearch focuses on the health information technology economics and the phar-maceutical industry.
Lesley Weaver, MPP, is a PhD student in the health services research, policy andadministration program at the University of Minnesota, School of Public Health,Division of Health Policy and Management.
Plan Characteristics Files Plan OOP drug costsPlan benefit design variables:deductible amount, has gap coverage
Identify drug plans’ cost-sharinginformation for OOP construction.
Drug plan ID
Abbreviations: ESRD, end end-stage renal disease; HIV, human immunodeficiency virus; ICD-9, International Classification of Diseases, Ninth Revision; LIS, low-incomesubsidies; NDC, National Drug Codes; OOP, out-of-pocket.
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