Roger Feldman, PhD

 To examine factors associated with discontinuation of new hepatitis C drugs—second-generation direct-acting antivirals (DAAs)&mdash;among Medicare beneficiaries with chronic hepatitis C.

 A retrospective analysis using 2014-2016 Medicare claims.

 The study population was patients with chronic hepatitis C in fee-for-service Medicare with Part D who initiated a DAA therapy between January 1, 2014, and September 1, 2016. We defined discontinuation of DAA therapy as filling prescriptions for fewer weeks than the expected duration of the DAA identified. We estimated adjusted odds ratios (aORs) of DAA discontinuation by patient characteristics using multivariable logistic regression. We estimated the model separately for patients with a Part D low-income subsidy (LIS) and those without an LIS.

 Of 82,056 patients who initiated a DAA therapy during the study period, 5171 (6.3%) did not complete the therapy. Discontinuation rates varied across DAAs, ranging from 4.7% (elbasvir/grazoprevir) to 11.8% (ombitasvir/paritaprevir/ritonavir/dasabuvir). Women with an LIS were more likely to discontinue DAA therapy than men with an LIS (aOR, 1.16; 95% CI, 1.08-1.25; 

 <.01). Non-LIS black and Hispanic patients had higher odds of discontinuation than non-LIS white patients (black: aOR, 1.49; 95% CI, 1.28-1.73; 

 <.01; Hispanic: aOR, 1.56; 95% CI, 1.01-2.44; 

 <.05). High comorbidity index score increased the odds of DAA discontinuation among patients with an LIS.

 Real-world discontinuation of DAA therapy was low, but it was 3 times more likely than in clinical trials and varied by patient characteristics. Efforts to increase DAA adherence would help lower patients’ risk of developing resistance to future treatments and reduce potential waste of resources.

We examined factors associated with discontinuation of new hepatitis C virus drugs—second-generation direct-acting antivirals (DAAs)&mdash;among Medicare beneficiaries with chronic hepatitis C between 2014 and 2016. We found:

Real-world discontinuation of DAA therapy among Medicare patients was low (6.3%), but it was about 3 times more likely than in clinical trials.

No racial/ethnic gap in DAA adherence existed among Medicare patients with a low-income subsidy (LIS); however, non-LIS black and Hispanic patients were more likely to discontinue a therapy than non-LIS white patients.

Discontinuation of DAA therapy was more likely among patients who had cirrhosis or more comorbidities.

Hepatitis C virus (HCV) infection is an important public health issue. It causes costly and serious liver diseases such as cirrhosis and liver cancer.

 HCV also aggravates other conditions, such as renal insufficiency, vasculitis, and rheumatoid arthritis, through its extrahepatic effects, decreasing patients’ quality of life and increasing economic burdens.

 In 2014, HCV caused more deaths in the United States than any other infectious disease, including HIV/AIDS.

The introduction of new HCV drugs—second-generation direct-acting antivirals (DAAs)&mdash;has provided an unprecedented opportunity to address the HCV epidemic.

 They are highly effective and easy to administer and have a short treatment period compared with earlier therapies. These features help patients complete the recommended course of DAAs, after which most patients are cured. Randomized trials found that only 1% to 2% of patients did not complete DAA therapy, which is a significant improvement over earlier HCV treatments.

 However, drug adherence in real-world practice may be poorer than in clinical trials because real-world patients are not as closely followed up as those in trials. High patient cost sharing for DAAs, a potential deterrent to therapy completion, is also likely in the real world.

Discontinuation of DAA therapy may limit the health benefits from the therapy and can place the patient at risk of developing resistance to future HCV treatments.

 Furthermore, given the high costs of DAAs, discontinuation of DAA therapy adds to financial burdens on the healthcare system without corresponding health benefits.

 It is thus important to monitor continuation of DAA prescriptions and identify patient characteristics related to prescription discontinuity. However, real-world evidence on DAA prescription continuity is limited. One study of commercial insurance enrollees reported that discontinuation of the first DAA (sofosbuvir, launched in December 2013) was 4 times higher than in clinical trials.

 Another study of private plan enrollees also indicated higher DAA discontinuation in the real world than in clinical trials.

 No information exists on discontinuation of DAAs in Medicare, which has been the largest payer of DAAs.

We examined factors associated with discontinuation of DAA therapy between 2014 and 2016 in a national cohort of Medicare patients with HCV. Many baby boomers—the group with the highest prevalence of HCV&mdash;are enrolled in Medicare.

 Medicare also covers patients with high HCV infection rates—nonelderly individuals who are disabled and/or dually eligible for Medicare and Medicaid. Discovering patient characteristics related to DAA discontinuation among Medicare patients can help clinicians and payers devise targeted interventions to improve drug adherence.

We identified all patients with HCV who were continuously enrolled in fee-for-service Medicare with Part D in a given year between 2014 and 2016 using Medicare data (

] lists diagnosis codes of all conditions used in the study). From these, we selected patients who initiated a DAA therapy between January 1, 2014, and September 1, 2016, and who were alive at least 4 months after initiating the therapy because most DAA users are on regimens with a therapy duration of 12 weeks or less. We used Medicare Prescription Drug Event files to identify initiation of 1 of the following DAAs: elbasvir/grazoprevir, ledipasvir/sofosbuvir, ombitasvir/paritaprevir/ritonavir plus dasabuvir, sofosbuvir, and sofosbuvir/velpatasvir. All DAAs except sofosbuvir are administered as monotherapy or in combination with ribavirin. Sofosbuvir is concomitantly used with peginterferon, ribavirin, simeprevir, or daclatasvir.

We obtained information on patient demographics and clinical characteristics from Medicare Beneficiary Summary Files.

We defined discontinuation of a DAA as filling prescriptions for fewer weeks than the expected duration of the DAA identified from package inserts or randomized trials. Discontinuation for patients who switched to a different DAA was based on the last DAA used by the patient. Discontinuation for patients on a regimen of a DAA plus ribavirin was based on only the DAA because ribavirin is used as an adjunct therapy to DAAs.

DAA regimens vary by genotype and prior HCV treatment experience, which could not be identified from Medicare claims. However, the recommended duration of therapy indicated by package inserts is 12 weeks for most patients regardless of DAA regimen. For patients who have prior experience with other HCV treatments, the recommended treatment duration is 24 weeks (16 weeks for elbasvir/grazoprevir). Results of recent randomized trials for patients without cirrhosis showed that 8-week treatments are effective for elbasvir/grazoprevir, ledipasvir/sofosbuvir, and ombitasvir/paritaprevir/ritonavir/dasabuvir.

 For these 3 DAAs, we required 8 weeks of treatment for patients without cirrhosis and 12 weeks for those with cirrhosis. For other DAAs, we applied 12 weeks to patients with and without cirrhosis. If patients filled prescriptions for more than 12 weeks (ie, patients were on regimens with expected duration >12 weeks), we applied 16 weeks for elbasvir/grazoprevir and 24 weeks for all other DAAs based on each drug’s package insert. We considered an interval between fills of fewer than 60 days as continuation of the therapy.

We used multivariable logistic regression to estimate adjusted odds ratios (aORs) of discontinuation by patient factors. We estimated the model separately for patients with and without a Part D low-income subsidy (LIS), which is offered to those with income below 150% of the federal poverty level.

 Cost sharing for non-LIS patients exceeded $6000 over the course of DAA treatment in all Part D plans in 2014.

 However, cost sharing was as low as $10.80 among patients with an LIS. We also estimated the model for the whole study population to determine the overall effects.

Patient characteristics were measured in the year of the DAA initiation. They included gender, age, race, urban/rural residence (

 refers to metropolitan areas identified by the Rural-Urban Continuum Codes

), cirrhosis status, and comorbidities. eAppendix Table 1 reports diagnosis codes or references of the established algorithms used to identify all the conditions used in the study. We also controlled for drug and Census region fixed effects.

We performed several sensitivity analyses. First, we estimated the logistic regression excluding patients with sofosbuvir plus peginterferon. This was the first new DAA regimen, but it is no longer recommended. Noncontinuation may be high with this regimen because of adverse effects of peginterferon. Second, we estimated the model separately for patients on regimens with and without ribavirin. Noncontinuation may be high for ribavirin-containing regimens because of adverse effects of ribavirin. Finally, we conducted analyses requiring patients to be alive at least 6 months after the initiation of the therapy because the expected duration of certain regimens was longer than 4 months.

We clustered the standard errors within Part D plans to account for potential correlations among beneficiaries subject to the same plan policies. We used SAS 9.4 (SAS Institute; Cary, North Carolina) for all analyses.

Of 82,056 patients who initiated a DAA therapy during the study period, 5171 (6.3%) did not complete the therapy (

). Compared with patients who completed DAA therapy, those who discontinued were more likely to have compensated cirrhosis (26.8% vs 18.9%; 

 <.001), decompensated cirrhosis (16.1% vs 9.5%; 

Discontinuation rates varied across DAAs, from 4.7% with elbasvir/grazoprevir to 11.8% with ombitasvir/paritaprevir/ritonavir plus dasabuvir (

). Sofosbuvir combined with peginterferon had a relatively high discontinuation rate (9.5%). The discontinuation rate was 5.2% among patients who used ledipasvir/sofosbuvir, the most commonly used DAA.

 presents the results from multivariable logistic regressions. The associations of gender and race with DAA discontinuation depended on LIS status. Female patients with an LIS were more likely to discontinue DAA therapy than male patients (aOR, 1.16; 95% CI, 1.08-1.25; 

 <.001). No differences in DAA discontinuation existed across racial/ethnic groups with an LIS. No gender gap in DAA continuation was present among non-LIS patients, but non-LIS black and Hispanic patients had higher odds of discontinuation than white patients (black: aOR, 1.49; 95% CI, 1.28-1.73; 

 <.001; Hispanic: aOR, 1.56; 95% CI, 1.01-2.44; 

The association between cirrhosis status and DAA continuation did not differ by LIS status, and in the entire sample, patients with compensated or decompensated cirrhosis had higher odds of discontinuation than those without cirrhosis (compensated: aOR, 1.79; 95% CI, 1.66-1.94; 

 <.001; decompensated: aOR, 1.92; 95% CI, 1.69-2.18; 

 <.001). High Charlson Comorbidity Index scores increased the odds of DAA discontinuation only among patients with an LIS (aOR, 1.02; 95% CI, 1.01-1.05; 

We found similar results from analyses excluding patients on sofosbuvir with peginterferon or requiring patients to be alive for at least 6 months after initiating a therapy (

). The analysis by ribavirin use showed that patients with anemia were more likely to discontinue regimens without ribavirin than those without anemia, but they were less likely to discontinue regimens with ribavirin (

Real-world discontinuation of DAA therapy among Medicare patients between 2014 and 2016 was 6.3%. The discontinuation rate varied across DAAs, but it was high for a DAA that required patients to take multiple pills (at different times) daily (eg, ombitasvir/paritaprevir/ritonavir plus dasabuvir). More DAAs with simpler regimens have become available in recent years. Discontinuation of DAA therapy may have decreased after the study period. However, continued assessment of population-level risk factors related to DAA discontinuation is needed to help clinicians make focused efforts on patients with risk factors, such as advising those patients to use specialty pharmacies, which has been shown to improve DAA completion.

We found differences in DAA continuation by patient demographics and health risk factors. Female patients with an LIS had a higher rate of DAA discontinuation than male patients. Prior studies of HCV treatments also found a gender gap: Women were less likely to initiate a DAA therapy than men,

 and among DAA users, women were less likely to complete a DAA therapy.

 Our study refines this existing knowledge by showing that the gender gap in Medicare is prominent among low-income beneficiaries. LIS patients have nominal cost sharing for DAAs.

 Thus, the gender gap in DAA adherence is likely due to certain characteristics of the LIS group (other than cost sharing) that are not observed in our data. Discovering reasons for that gap was beyond the scope of our study. However, it is an important inquiry for future research to pursue, and efforts are needed to explore ways to close the gender gap in DAA continuation.

We found no racial/ethnic differences in DAA adherence among patients with an LIS. However, racial/ethnic minority patients without an LIS were more likely to discontinue DAA therapy than white non-LIS patients. The non-LIS group includes patients with relatively low incomes who may have had high financial burdens from DAA therapy. Prior literature showed that generous insurance benefits (eg, cost-sharing reductions or coverage expansions) for relatively low-income people improved service use or prescription drug adherence more in racial/ethnic minorities than in whites.

 Expanding cost-sharing subsidies for DAAs, which are highly effective, to patients who do not currently qualify for an LIS may help increase adherence to those treatments and mitigate racial/ethnic disparities.

Finally, discontinuation was more likely among patients with cirrhosis or other comorbidities. Patients with cirrhosis have longer treatment periods with more refills, which may increase the likelihood of discontinuation. Sicker patients with more comorbidities may have difficulties in completing DAA therapy because of competing demands for treating other conditions, or they may have experienced worsening prognosis while on DAA therapy, leading them to stop the therapy.

We note several limitations of the study. First, Medicare claims lack detailed clinical data, such as fibrosis stage and genotype. We could not incorporate those clinical factors, which may be related to DAA discontinuation. Second, we could not distinguish physician-initiated DAA discontinuation due to worsening prognosis from patient nonadherence. However, our findings can help clinicians gain insights into where to focus efforts to improve DAA completion. Finally, our study has limited generalizability to Medicare Advantage plans, commercial insurance settings, and newer DAAs that became available after our study period.

Despite these limitations, we offer the first national estimate of DAA discontinuation among Medicare patients in real-world practice. We also provide information on patient demographic and risk factors that clinicians and payers could target to increase DAA adherence. Continued efforts to monitor and improve DAA adherence would help lower patients’ risk of developing resistance to future treatments and reduce potential waste of resources while increasing patients’ health benefits.

 Department of Health Policy and Administration, College of Health and Human Development (JJ), The Pennsylvania State University, University Park, PA; Department of Public Health Sciences (PD) and Department of Medicine (TR), College of Medicine, The Pennsylvania State University, Hershey, PA; Division of Health Policy and Management, School of Public Health, University of Minnesota (RF), Minneapolis, MN.

 National Institutes of Health/National Institute of Aging grant number R01 AG055636-01A1 and National Institutes of Health grant number R24 HD041025.

 Dr Feldman owns a small amount of stock in Gilead Sciences. The remaining 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 (JJ, PD, RF, TR); acquisition of data (JJ); analysis and interpretation of data (JJ, PD, RF, TR); drafting of the manuscript (JJ, PD, TR); critical revision of the manuscript for important intellectual content (PD, RF, TR); obtaining funding (JJ, PD, RF); and supervision (TR).

 Jeah Jung, PhD, Department of Health Policy and Administration, College of Health and Human Development, The Pennsylvania State University, 601E Ford Bldg, University Park, PA 16801. Email: kuj11@psu.edu.

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3. Negro F. Facts and fictions of HCV and comorbidities: steatosis, diabetes mellitus, and cardiovascular diseases. 

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4. Gill K, Ghazinian H, Manch R, Gish R. Hepatitis C virus as a systemic disease: reaching beyond the liver. 

. 2016;10(3):415-423. doi: 10.1007/s12072-015-9684-3.

5. Younossi Z, Park H, Henry L, Adeyemi A, Stepanova M. Extrahepatic manifestations of hepatitis C: a meta-analysis of prevalence, quality of life, and economic burden. 

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6. Klevens RM, Hu DJ, Jiles R, Holmberg SD. Evolving epidemiology of hepatitis C virus in the United States. 

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7. Hepatitis C kills more Americans than any other infectious disease [news release]. Atlanta, GA: CDC; May 4, 2016. cdc.gov/media/releases/2016/p0504-hepc-mortality.html. Accessed January 6, 2020.

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Articles

Real-world discontinuation of hepatitis C drugs was low, but it was 3 times more likely than in clinical trials and varied by patient characteristics.

Discontinuation of New Hepatitis C Drugs Among Medicare Patients

It is unfortunate that the key approach and message of our article were not received by the authors of the letter. We use this opportunity to clarify our risk-adjustment methods and to discuss the importance of continuing work on the topic.

The primary aim of our research was to provide the first information on risk-adjusted spending and utilization of chemotherapy by care site in Medicare because most prior work on this topic examined the commercial sector. We focused on chemotherapy drugs and administration, so researchers will be aware of differences in site-specific chemotherapy use and costs between Medicare and commercial settings. Future studies can expand our analysis to include cancer services other than chemotherapy.

Regarding the risk-adjustment methods, we lacked information on cancer stage. This is a well-known limitation of claims data that also affected the Winn et al

 study of the commercial setting. However, we adjusted for a comprehensive set of risk factors, including indicators of comorbid illnesses that are common among chemotherapy users (eg, heart disease, diabetes, hypertension, depression, chronic pulmonary disease). Moreover, we used cancer-specific risk factors, including cancer metastasis, cancer type, and prior-year cancer-related utilization variables, which are strong predictors of chemotherapy service use and spending. The authors of the letter mentioned the Deyo-Charlson Index, which predicts general health outcomes, including mortality, as a possible adjuster. However, we believe that the cancer-specific risk factors we used are more appropriate and better adjusters for the analysis of chemotherapy services.

It is also surprising that the authors of the letter viewed our analysis of and adjustment for differences in the distribution of cancer types between hospital outpatient departments (HOPDs) and physician offices as a limitation of the study. Most prior comparisons of cancer costs between HOPDs and physician offices did not adjust for differences in cancer types by setting. We clearly pointed out those differences and discussed the importance of adjusting for them by showing how the results changed after adjustment. This is a main contribution and takeaway of our study.

For the facility fees in HOPDs, the Medicare outpatient prospective payment system rates for spending on chemotherapy administration in HOPDs include hospital facility fees.

 Thus, using the Medicare payment rate to calculate chemotherapy administration spending in HOPDs is appropriate and incorporates the facility fees.

Finally, differences in payments by site of care are relatively small in Medicare compared with the commercial sector. However, Medicare is an important payer for cancer-related services, being responsible for one-third of all cancer costs.

 Given Medicare’s role in cancer care, Medicare cancer costs are an important issue to analyze. We offered the first evidence on site-specific Medicare spending for chemotherapy agents and administration. As the site of care continues to shift from physician offices to HOPDs, chemotherapy costs and total cancer costs by site will continue to be important in both Medicare and commercial settings. We hope to see future studies that address this issue.

 Pennsylvania State University (YK, JJ), University Park, PA; University of Minnesota (RF), Minneapolis, MN.

 None; the original research was supported by NIH/NIA grant number 1R01AG047934-01 and NIH grant number R24 HD041025.

 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 (YK, RF); analysis and interpretation of data (RF); drafting of the manuscript (YK, JJ); critical revision of the manuscript for important intellectual content (JJ, RF); administrative, technical, or logistic support (YK, JJ); and supervision (JJ). 

 Yamini Kalidindi, MHA, Department of Health Policy and Administration, Pennsylvania State University, 604 Ford Bldg, University Park, PA 16802. Email: ypk100@psu.edu.

1. Winn AN, Keating NL, Trogdon JG, Basch EM, Dusetzina SB. Spending by commercial insurers on chemotherapy based on site of care, 2004-2014. 

. 2018;4(4):580-581. doi: 10.1001/jamaoncol.2017.5544.

2. Guidi TU. Medicare’s Hospital Outpatient Prospective Payment System: OPPS 101 (part 2 of 2). 

. 2011;7(1):57-60. doi: 10.1200/JOP.2010.000191.

3. Chapter 3: hospital inpatient and outpatient services. In: Medicare Payment Advisory Commission. 

Report to the Congress: Medicare Payment Policy

4. Part B drugs payment systems. Medicare Payment Advisory Commission website. medpac.gov/docs/default-source/payment-basics/medpac_payment_basics_17_partb_final.pdf?sfvrsn=0. Published October 2017. Updated October 2017. Accessed August 22, 2018.

5. Total expenses and percent distribution for selected conditions by source of payment: United States, 2014. Agency for Healthcare Research and Quality website. meps.ahrq.gov/data_stats/tables_compendia_hh_interactive.jsp?_SERVICE=MEPSSocket0&_PROGRAM=MEPSPGM.TC.SAS&File=HCFY2014&Table=HCFY2014_CNDXP_D&_Debug=. Accessed August 22, 2018.

Our study provides the first evidence on site-specific Medicare spending on chemotherapy adjusting for patient comorbid illnesses, cancer type, and other cancer-related risks.

The Authors Respond and Stand Behind Their Findings

 To compare Medicare spending on provider-administered chemotherapy in hospital outpatient departments (HOPDs) and physician offices after controlling for cancer type.

 We used 2010-2013 claims data for a random sample of Medicare fee-for-service beneficiaries who had cancer and received chemotherapy services either in physician offices or in HOPDs. We constructed 2 spending measures: (1) spending on chemotherapy drugs and (2) spending on chemotherapy administration. Each spending measure was the allowed payment, which includes both Medicare reimbursement and patient out-of-pocket spending. We compared the spending measures in the 2 care settings using regression analysis to control for certain patient risk factors, including cancer type. We also compared the number of chemotherapy and administration claims per beneficiary and spending per claim by cancer type to understand differences in utilization patterns in the 2 care settings.

 Risk-adjusted chemotherapy drug spending per beneficiary was $2451 lower in HOPDs compared with physician offices. Risk-adjusted chemotherapy administration spending was $322 higher in HOPDs than in physician offices. Patients in physician offices received chemotherapy drugs more frequently than those in HOPDs. However, the chemotherapy spending per claim line was higher in HOPDs than physician offices.

 Chemotherapy drug spending per Medicare beneficiary was lower in HOPDs than in physician offices, driven by less frequent use of chemotherapy in HOPDs. As the site of provider-administered chemotherapy shifts from physician offices to HOPDs, continuing assessment of cancer care spending by site of care is necessary.

Using 2010-2013 Medicare claims data, this study's results demonstrate that:

Spending on chemotherapy drugs was $2451 lower for Medicare beneficiaries receiving chemotherapy in hospital outpatient departments (HOPDs) than in physician offices.

The spending on chemotherapy administration was $322 higher for Medicare beneficiaries receiving chemotherapy in HOPDs than in physician offices.

As chemotherapy infusions are increasingly provided in the hospital outpatient setting, policy makers and payers should be aware that this shift in the site of chemotherapy may influence cancer care patterns and spending.

Chemotherapy is a common cancer treatment modality and a significant contributor to the cost of cancer treatment.

 Many chemotherapy drugs are available in injectable forms, which are administered by providers in clinical settings.

 Provider-administered drugs are usually reimbursed under the medical benefit of an insurance policy instead of the pharmacy benefit.

 In Medicare, they are reimbursed by Part B coverage for outpatient medical services. Providers purchase drugs and then submit claims to Medicare for reimbursement of the drugs and associated administration costs.

 Most Part B—covered drugs are administered in physician offices or hospital outpatient departments (HOPDs).

Over the past decade, the site of provider-administered cancer drugs has shifted from physician offices to HOPDs.

 This trend has led to a concern that cancer care costs may increase because of differences in care costs between HOPDs and physician offices. Spending on cancer care in commercial settings is considerably higher in HOPDs than in physician offices, mainly due to higher payment rates for chemotherapy drugs and other services in HOPDs.

However, these findings may not apply to Medicare. Medicare typically reimburses hospitals and physicians the same fee for Part B—covered drugs: 106% of the manufacturer’s average sales price (ASP; the budget sequestration of 2013 reduced payments received by providers to 104.3% of ASP).

 No consistent pattern exists in Medicare’s reimbursement for drug administration. Some administration codes are paid more in HOPDs, whereas others are paid more in physician offices. In general, payments are higher in HOPDs. For example, in 2011, 14 of 20 administration codes payable in both settings were paid more in HOPDs.

 However, administration fees are much smaller than chemotherapy drug costs. Thus, differences in chemotherapy-related costs in Medicare Part B mainly come from differences in chemotherapy drug utilization, such as the quantity of chemotherapy or use of more expensive chemotherapeutic agents.

A report by The Moran Company compared spending on chemotherapy between HOPDs and physician offices in Medicare using 2009-2011 claims data.

 The report documented that the average number of chemotherapy claims per patient was slightly higher in HOPDs than in physician offices and average spending per patient on chemotherapy agents was substantially higher in HOPDs. Based on these findings, the Moran report concluded that more and costlier chemotherapy treatments are used in HOPDs than in physician offices, given the same Medicare fees for chemotherapy drugs in both settings. However, the Moran analysis did not adjust for differences in patient risk factors between the 2 settings. An important risk factor is cancer type. The distribution of cancer types differs by setting,

 and cancer drug utilization patterns differ by cancer type.

 If patients with specific cancer types requiring expensive chemotherapy are more likely to be treated in HOPDs, the Moran report’s conclusion is not valid.

To our knowledge, no study has examined chemotherapy-related spending in Medicare Part B after controlling for patient characteristics, such as cancer type. Our study fills this gap. We compared chemotherapy drug and administration spending in HOPDs and physician offices after controlling for cancer type. In addition, we explored differences in chemotherapy utilization patterns between the 2 settings.

The primary data sources were the 2010-2013 Medicare Outpatient file, which contains records for services in HOPDs, and the 2010-2013 Medicare Carrier file, which has claims for services by noninstitutional providers. Both files contain information on diagnosis, Healthcare Common Procedure Coding System (HCPCS) code, service date, and payments. Medicare Master Beneficiary Summary files provided beneficiaries’ demographic characteristics and disease indicators, including cancer type, and the American Community Survey supplied zip code—level income, education, and unemployment rates.

The study population is a random sample of Medicare fee-for-service beneficiaries with cancer between 2010 and 2013. To select the sample, CMS first identified all patients with cancer from 100% of Medicare claims based on the standard algorithm used to create cancer indicators in the Medicare Chronic Condition Warehouse: having at least 1 inpatient or skilled nursing facility claim with a cancer diagnosis or at least 2 Carrier or Outpatient claims with a cancer diagnosis in a given year. Next, CMS provided us with the data for a random sample of those patients.

We restricted the sample to patients who had at least 1 chemotherapy claim identified by HCPCS Level II (J-codes) in the Outpatient or Carrier data. Chemotherapy includes all antineoplastic drugs (immune, hormonal, and targeted therapy). We selected claims with both cancer diagnosis and chemotherapy J-codes to exclude cases using cancer drugs for other conditions. All cancer diagnosis codes and chemotherapy J-codes used are reported in 

). Claims for chemotherapy reported in both Carrier and Outpatient files using the same J-code on the same day were considered duplicates, and duplicate claims in the Carrier file were excluded to avoid double counting. We considered Carrier claims with the service place code of HOPDs as HOPD claims.

We further restricted the sample to patients with cancer who had both Medicare Part A and Part B coverage for the full year, and we excluded those who died within 3 months of diagnosis. We excluded enrollees in Medicare Advantage plans because their claims data are not available to researchers.

The study sample was categorized into 2 groups depending on the site of chemotherapy administration: HOPD-only if they received chemotherapy in HOPDs only and office-only if they received chemotherapy in physician offices only. Patients receiving chemotherapy in both settings, who accounted for 4.4% of the sample, were excluded to make a clean comparison of costs between HOPDs and physician offices.

We constructed 2 outcome measures: (1) chemotherapy drug spending per beneficiary and (2) chemotherapy administration spending per beneficiary. These spending variables are allowed payments, including both Medicare reimbursements and patient out-of-pocket spending. We constructed chemotherapy drug spending by summing the allowed payments across each patient’s chemotherapy claims with a cancer diagnosis code. Chemotherapy administration spending was created as the sum of the allowed payments across each patient’s claims with chemotherapy administration codes and a cancer diagnosis.

We began with a descriptive analysis of 6 cancer types (prostate, breast, lymphoma, colon, lung, and leukemia) for which Part B chemotherapy is frequently used. First, we compared the distribution of cancer types between HOPDs and physician offices. Second, we compared chemotherapy drug and administration spending per beneficiary between the 2 settings for the entire sample and for each cancer type.

We used a linear regression model with clustered standard errors within a zip code. Our unit of analysis was a patient-year. The dependent variables were chemotherapy drug spending and chemotherapy drug administration spending. The key explanatory variable was a binary indicator equal to 1 if the patient received provider-administered chemotherapy only in HOPDs and 0 if she/he received chemotherapy only in physician offices. Key control variables were cancer type indicators, an indicator of cancer metastasis, and the number of cancer-related hospitalizations and outpatient visits in the prior year. To identify metastasis, we used the criterion of at least 2 diagnosis codes of metastatic disease (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] codes 196-199) separated by 30 days or more.

The regression also controlled for patient age, gender, race, state buy-in status (an indicator of whether Medicaid pays the patient’s Part B premium), indicators of chronic conditions (ischemic heart disease, diabetes, hypertension, hyperlipidemia, depression, heart failure, chronic pulmonary disease, and cataract), and number of chronic conditions. Area-level variables were average income, percent college educated, and unemployment rates at the zip code level. Finally, we used year dummies to control for year-specific effects that are common to all patients.

Using the regression results, we obtained risk-adjusted spending in each setting. To calculate risk-adjusted chemotherapy drug and administration spending in physician offices, we computed predicted spending by setting the HOPD indicator to 0 and all other covariates to their mean values. Similarly, we obtained risk-adjusted spending in HOPDs by computing predicted spending with the HOPD indicator equal to 1 and the means of all other covariates.

To explore whether chemotherapy utilization patterns differed between HOPDs and physician offices, we compared the number of chemotherapy and administration claims per beneficiary by cancer type. We also assessed spending per claim for chemotherapy drug and administration by cancer type.

We performed the following sensitivity checks. First, we performed the regression analysis by year to check if differences in a particular year were driving the overall regression results. We used the same variables as in the primary analysis (except year-specific dummies) and calculated risk-adjusted spending in HOPDs versus physician offices for each year.

Second, we performed the regression analysis separately for each of the 6 cancers (prostate, breast, lymphoma, colon, lung, and leukemia) to check if the results were consistent across major cancer types.

Third, we limited the analysis to separately reimbursable chemotherapy drugs (drugs that are not bundled into a payment group under the Medicare Hospital Outpatient prospective payment system). Medicare determines separately reimbursable drugs based on a threshold daily cost (>$80 in 2013). Chemotherapy drugs whose daily costs are below the threshold are considered a dependent or ancillary service to the drug administration. Their cost is “bundled” into an Ambulatory Payment Classification

 and arbitrarily allocated by hospitals. Including them in the analysis may lower the estimates of chemotherapy drug spending per beneficiary in HOPDs. We thus excluded nonseparately reimbursable drugs from both the Carrier and Hospital Outpatient files and checked the sensitivity of the results.

codes of surgeries for certain cancers for which there is evidence of better outcomes.

 Individuals who underwent these surgeries are likely to use chemotherapy drugs as adjuvant therapy. We conducted the regression analysis on this subpopulation, who were relatively homogenous in terms of cancer severity, and checked the sensitivity of the results.

 shows the distribution of cancer types among Medicare chemotherapy users in HOPDs and physician offices. Six cancers accounted for more than 90% of all chemotherapy users in both HOPDs and physician offices, but the distribution of cancer types differed by setting. Prostate cancer accounted for 25% of chemotherapy users in HOPDs but more than 55% of chemotherapy users in physician offices. The bottom panel of Figure 1 indicates that physician offices were the dominant place of services for all cancer types. Eighty-four percent of patients with prostate cancer received chemotherapy in physician offices, and about 60% of patients with other cancers used physician offices.

 shows that unadjusted average chemotherapy drug spending per beneficiary in the entire sample was about 34% higher in HOPDs than in physician offices ($15,058 vs $11,219). However, chemotherapy drug spending for patients with the same cancer type was higher in physician offices than in HOPDs for most cancer types except prostate cancer (bottom panel of Figure 2).

 depicts descriptive data on chemotherapy administration spending per beneficiary. Unadjusted average chemotherapy administration spending per beneficiary was higher in HOPDs compared with physician offices, both in the full sample and among patients with the same cancer type.

 presents risk-adjusted chemotherapy spending in each setting based on the regression results. Risk-adjusted chemotherapy drug spending showed very different patterns than unadjusted spending. Chemotherapy drug spending per beneficiary after risk adjustment was $2451 lower in HOPDs than in physician offices ($10,658 vs $13,109). Risk-adjusted chemotherapy administration spending per beneficiary was $322 higher in HOPDs compared with physician offices ($1543 vs $1221).

 reports the number of claims per beneficiary and spending per claim on chemotherapy drugs and administration by cancer type. These data help explain why chemotherapy drug spending was lower in HOPDs compared with physician offices after controlling for cancer type. The frequency of chemotherapy among chemotherapy users with the same cancer type was higher in physician offices than HOPDs for most cancer types except prostate cancer. For example, patients with colon cancer had 19 chemotherapy drug claims per beneficiary in physician offices versus 13 in HOPDs, on average. On the other hand, spending per claim among patients with colon cancer was $367 higher in HOPDs compared with physician offices. For other cancers, spending was between $257 and $737 higher in HOPDs than in physician offices. Thus, the difference in spending per claim between HOPDs and physician offices is much smaller compared with the difference in spending from adding 1 more claim, which exceeds $1000 across all cancer types in both settings. These data imply that additional drug claims are an important driver of total chemotherapy drug spending per patient, and more frequent use of chemotherapy led to higher spending in physician offices than HOPDs, after controlling for cancer type.

Similarly, the number of chemotherapy administration claims per beneficiary was higher in physician offices than in HOPDs for most cancer types, except prostate cancer. On the other hand, average spending per administration claim was almost twice as high in HOPDs compared with physician offices for most cancer types. For example, spending per chemotherapy administration claim for colon cancer was $182 in HOPDs compared with $100 in physician offices. This difference in spending per claim is large, considering that most administration claims were less than $200. Thus, higher chemotherapy administration costs per beneficiary in HOPDs compared with physician offices are largely driven by more costly administration claims in HOPDs.

The results of the sensitivity analysis supported the findings described above. Across all years, risk-adjusted chemotherapy drug spending per beneficiary was lower in HOPDs than in physician offices, and risk-adjusted chemotherapy administration spending per beneficiary was consistently higher in HOPDs compared with physician offices (

). Results from the analysis of each cancer (

) were also consistent with the main analysis. The analysis using only separately reimbursable chemotherapy produced very similar results to the primary analysis (

). Risk-adjusted chemotherapy drug spending per beneficiary was $2245 lower in HOPDs than in physician offices, driven by the smaller number of separately reimbursable chemotherapy claims in HOPDs. The analysis using patients undergoing a cancer-related surgery also produced results consistent with the main analysis.

Analyzing 2010-2013 Medicare claims data, we found that risk-adjusted chemotherapy drug spending was lower for patients receiving chemotherapy in HOPDs than for patients using physician offices. We explored 2 contributors to these differences: differences in the number of chemotherapy drug claims and spending per chemotherapy claim. We found that chemotherapy users in physician offices received more chemotherapy than those in HOPDs for most cancer types and that average spending per chemotherapy drug claim was slightly higher in HOPDs than physician offices for all cancer types. However, the differences in average spending per claim were not large enough to make substantial differences in total chemotherapy drug spending per beneficiary. These findings indicate that lower utilization per beneficiary was an important driver of lower risk-adjusted chemotherapy drug spending in HOPDs than in physician offices.

Our findings differ from those of the Moran report, which concluded that more and costlier chemotherapy treatments are used in HOPDs than in physician offices.

 It is important to note that the Moran report did not adjust for patient risk factors, including cancer type. As our results and prior literature indicate, the distribution of cancer types differs by setting

 Further, our data indicated that the frequency of chemotherapy among chemotherapy users with the same cancer was higher in physician offices than HOPDs for most cancer types except prostate cancer. Thus, adjusting for cancer type is of utmost importance in explaining the cost and utilization differences between the 2 settings.

Our findings also differ from prior research in commercial settings, which consistently found that chemotherapy costs were higher in HOPDs than physician offices.

 However, as mentioned earlier, spending differences in commercial settings are driven by price differences between HOPDs and physician offices rather than differences in the quantity of services.

 Medicare uses the same reimbursement rates for chemotherapy drugs in both settings. It is thus not surprising that chemotherapy spending per Medicare beneficiary is lower in HOPDs than physician offices.

Our analysis also showed that higher chemotherapy spending in physician offices was due to higher utilization. This result is consistent with research in commercial settings. Hayes et al found that the mean number of chemotherapy sessions in employer-sponsored plans was higher in community oncology clinics than in HOPDs.

 To our knowledge, our analysis is the first to explore differences in chemotherapy utilization by care setting and cancer type in Medicare.

We note several limitations of our study. First, we did not consider costs for other services that patients may have used when receiving chemotherapy. Prior research suggests that patients visiting HOPDs are likely to receive additional services (eg, laboratory tests) that might not be offered in physician offices.

 We did not analyze spending on those services. Second, our findings are not generalizable to the commercial sector, where payment rates for chemotherapy drugs differ substantially by care site.

 Third, we could not completely adjust for cancer severity, such as cancer stage, because detailed clinical information is not available in Medicare data. We partially addressed this issue by using a metastasis indicator, but our approach of identifying metastasis from diagnosis codes may have limited validity.

 Third, the choice of chemotherapy site could depend on patients’ preferences. Patients may prefer to use HOPDs because of the availability of other services or a short travel distance. Such patient characteristics might be related to chemotherapy use and spending to some extent. However, our study did not control for those factors. Finally, there was a shift in the site of cancer care from office-based to HOPD-based due to hospitals’ acquisition of physician practices during the study period.

 Although examining chemotherapy use and spending in those practices acquired by hospitals would be informative, it is beyond the scope of our analysis, and we leave it to future research.

Chemotherapy drug spending per Medicare beneficiary was lower in HOPDs than in physician offices, driven by less frequent use of chemotherapy. As the site of provider-administered chemotherapy shifts from physician offices to HOPDs, continuing assessment of cancer care spending by care site is necessary.

 Department of Health Policy and Administration, Pennsylvania State University (YK, JJ), University Park, PA; Division of Health Policy and Management, University of Minnesota (RF), Minneapolis,

Spending on chemotherapy drugs was lower among Medicare beneficiaries who received chemotherapy in hospital outpatient departments than among comparable beneficiaries receiving chemotherapy in physician offices.

Differences in Spending on Provider-Administered Chemotherapy by Site of Care in Medicare

 The recent arrival of new hepatitis C virus (HCV) drugs has brought fiscal pressures onto Medicare Part D; spending on HCV drugs in Part D jumped from $283 million in 2013 to $4.5 billion in 2014. We examined the current benefit designs for HCV drugs in Part D plans and analyzed patients’ financial burden for those drugs

A cross-sectional analysis of CMS’ July 2015 Part D Plan Formulary File and the Wolters Kluwer Health Medi-Span Electronic Drug File v.2.

We analyzed the type and amount of cost sharing for HCV drugs and the extent to which plans apply utilization management tools. We then estimated total out-of-pocket spending for beneficiaries to complete a course of treatment

All Part D plans covered at least 1 recently introduced 

 drug, as of July 2015. Nearly all plans charged relatively high coinsurance and required prior authorization for new 

 For enrollees with a low-income subsidy, out-of-pocket spending varies between 

For enrollees with no subsidy, the mean out-of-pocket spending needed to complete a course of treatment is substantial, ranging from $6297 to $10,889.

 drug users with no subsidy face sizable financial burdens, even with catastrophic coverage and the recent in-gap discount for brand name drugs. 

As baby boomers—the group most likely to have HCV&mdash;join Medicare, efforts should be made to ensure patient access to these needed drugs.

Am J Manag Care. 2016;22(5 Spec Issue No. 6):SP220-SP226

	The high prices of new hepatitis C virus (HCV) drugs are bringing fiscal pressures onto Medicare. The current coverage designs for HCV drugs by Medicare Part D plans are:

		All Part D plans cover at least 1 new, expensive HCV drug.

		Nearly all plans charge relatively high coinsurance and require prior authorization.

		Expected out-of-pocket spending for enrollees with no subsidy to complete a course of treatment ranges from $6297 to $10,889; for enrollees eligible for a low-income subsidy, total expected out-of-pocket spending varies between $10.80 and $1191.

		Under the current Part D benefits, HCV drug users with no subsidy face sizable financial burdens.

Prescription drug spending in the United States increased by 13.1% in 2014—the highest rate for the decade thus far&mdash;driven by a 30.9% hike in specialty drug spending.

 Among specialty drugs, Sovaldi (sofosbuvir) is considered a major contributor to the 2014 increase in drug spending,

 and since its arrival, this new drug for hepatitis C virus (HCV) has drawn intense attention from the media, policy makers, and researchers. Despite Sovaldi’s novel aspects, its high price tag has been at the center of the discussion, igniting debates over how much our society is willing to pay for innovative prescription drugs. Two additional HCV drugs—Harvoni (a combination of ledipasvir and sofosbuvir) and Viekira Pak (ombitasvir/paritaprevir/ritonavir co-packaged with dasabuvir)&mdash;entered the market with similarly high prices in late 2014.

These HCV drugs are not an isolated case: highly effective, yet extremely expensive, drugs for other diseases are increasingly being introduced to market. Nevertheless, HCV drugs present a clear example of the fiscal pressures that new drugs are imposing on the healthcare system. The financial impact of the new HCV drugs has been particularly salient in Medicare Part D, where spending on these drugs jumped from $283 million in 2013 to $4.5 billion in 2014.

 Spending on Sovaldi alone—the drug with the highest spending in Part D&mdash;exceeded $3 billion.

This study analyzes the current coverage designs for hepatitis C virus drugs by Medicare Part D plans.

Author | Roger Feldman, PhD

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