Journal of Medical Economics
ISSN: 1369-6998 (Print) 1941-837X (Online) Journal homepage: http://www.tandfonline.com/loi/ijme20
Essential need for research in hepatitis C J. S. McCombs To cite this article: J. S. McCombs (2015) Essential need for research in hepatitis C, Journal of Medical Economics, 18:7, 512-513 To link to this article: http://dx.doi.org/10.3111/13696998.2015.1017502
Accepted author version posted online: 17 Feb 2015. Published online: 05 Mar 2015. Submit your article to this journal
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Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ijme20 Download by: [University of Lethbridge]
Date: 14 November 2015, At: 12:18
Journal of Medical Economics 1369-6998 doi:10.3111/13696998.2015.1017502
Vol. 18, No. 7, 2015, 512–513
Article 0121.R1/1017502 All rights reserved: reproduction in whole or part not permitted
Letter to the editor Essential need for research in hepatitis C
Downloaded by [University of Lethbridge] at 12:18 14 November 2015
J. S. McCombs Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, School of Pharmacy, Leonard Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, USA
Letter in response to: Tandon N, Balart LA, Laliberte´ F, Pilon D, Lefebvre P, Germain G, Prabhakar A. Impact of completing chronic hepatitis C (CHC) treatment on posttherapy healthcare cost. J Med Econ 2014;17:862–71 Dear Editor,
Address for correspondence: J. S. McCombs, PhD, Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, School of Pharmacy, Leonard Schaeffer Center for Health Policy and Economics, University of Southern California, 635 Downey Way, VPD 212B Los Angeles, CA 90089-3333, USA. [email protected] Accepted: 6 February 2015; published online: 5 March 2015 Citation: J Med Econ 2015; 18:512–3
512
Letter to the editor McCombs
Clinicians and policy-makers need evidence with which to make decisions on how to most effectively employ new technologies as they are introduced into clinical practice. This need for cost-effectiveness information is particularly acute when a new treatment competes with older, effective alternatives. The cost-effectiveness of a new technology depends critically on the effectiveness of older therapies in meeting the clinical needs of real world patients. These ‘essential need’ data are obtained using data from paid claims or electronic medical records systems, analyzed using observational research designs, and multivariate statistical techniques. Additional data on incidence, prevalence, and costof-illness are generally available in the literature, as are studies estimating the impact of the disease state on the patient’s quality-of-life and work productivity. At product launch, these diverse sources of essential need information are combined with safety and efficacy data derived from the randomized clinical trials (RCTs) into computerized cost-effectiveness models which attempt to predict the fiscal and clinical impact of the new technology. Once a product is launched, clinicians and policy-makers need information on how well the new technology is meeting the essential need documented prior to product launch; however, it is uncommon that a technology innovator will conduct an ongoing program of research to document if their new technology performed as projected in their CEA models. That is, the innovator company generally leaves this task of evaluating the real-world impact of their technology to large ‘buyers’, such as HMOs. Unfortunately, the results of these internal studies by HMOs and other buyers may not be published. The task of documenting how well a new technology met the essential need at its launch will be eventually taken up by second-to-market companies in an effort to document the essential need for their competing products under development; however, it is common that a cluster of newer treatments will be launched in a span of a few years, which limits the ability of second-to-market companies to document the effectiveness of the innovator product due to lack of available data. This is the case in hepatitis C, and the paper by Tandon et al.1 must be evaluated in this context. Tandon et al. focus their analysis on the evaluation of ‘the healthcare resource utilization and cost alleviation associated with completing a 36–48 week HCV therapy using all of the currently available data from a large national healthcare claims database’ (p. 863, col. 1). To accomplish this objective, the authors use claims data from OptumHealth Reporting and Insights Database for January www.informahealthcare.com/jme ! 2015 Informa UK Ltd
Downloaded by [University of Lethbridge] at 12:18 14 November 2015
Journal of Medical Economics
2001 through to March 2013. These data include medical and pharmacy claims of more than 13 million privatelyinsured individuals covered by 60 self-insured Fortune 500 companies with locations in all census areas of the US. The data from OptumHealth do not include data on viral load or viral genotype that are critical for determining when an adequate course of therapy for hepatitis C has been achieved. Clinical studies have documented that patients with genotypes 2 and 3 require 28 weeks of interferon/ribavirin treatment compared to the 48 weeks required to effectively treat genotype 1 and 4. In response to these limitations in data, Tandon et al. excluded patients with 20–28 weeks of uninterrupted interferon therapy in an effort to remove patients with genotype 2 or 3 who may have completed an adequate course of HCV therapy. This step was necessary to remove these ‘completers’ from the population of patients who ‘fail’ treatment achieving less the 36 weeks of uninterrupted therapy. Tandon et al. elected to define an observation period for measuring costs that ‘spanned from 48 weeks after the index date to the earliest date between the end of insurance coverage or to the end of data availability’ (p. 863, col. 2). Stated bluntly, Tandon et al. ignore all cost of treatment incurred during the first 48 weeks, even as they use data from this period to calculate duration of therapy. While it is comforting to see that costs in the period following treatment have been diminished for patients who achieve 36–48 weeks of therapy, data on the costs associated with drug therapy adherence are needed to determine how the cost of the completed treatment compare to the saving attributed. An earlier paper2 documented that first year treatment cost increased significantly, primarily due to increased drug costs ($10,617 for completers of 24–48 weeks of therapy; $27,116 for patients with 48þ weeks of therapy). In the second year, total costs were reduced by $6171 and $8130, respectively. These latter results are consistent with the findings by Tandon et al.1. There are a few oddities reported in Tandon et al. that indicate potential confounding in this study’s attempt to measure the impact of 48þ weeks of continuous therapy on post-treatment costs. Tandon et al. report data for the proportion of days covered in Table 2—a well-accepted measure of adherence and patients who completed 48þ weeks of therapy have a mean ‘exposure to therapy’ of 489 days; however, patients defined as non-completers average 391 days of exposure. This may indicate that many non-compliers likely started and stopped therapy multiple times. This significant exposure to treatment by non-completers is likely to reduce their costs, thus narrowing the differences in costs over the post-treatment period. A sensitivity
! 2015 Informa UK Ltd www.informahealthcare.com/jme
Volume 18, Number 7
July 2015
analysis should have been investigated using PDC to define adherence, which would have provided a second measure of the impact of treatment on future costs. It is unfortunate that researchers bypass opportunities to document additional and important parameters of ‘essential need’, despite having access to adequate data. For example, in Tandon et al., only 5004 of 30,307 patients with a diagnosis of CHC initiated interferon treatment (16.5%). This treatment rate exceeds the 5.3% treatment rate reported based on an analysis of a commercial database2, but is lower than the 24.3% treatment rate recently reported for VA patients with a detectable viral load3. While these reported treatment rates are low, these three studies do not provide any analysis on the predictors of treatment initiation or treatment response, such as achieving an undetectable viral load. This leaves a gap in the data needed to accurately project the potential impact of the new hepatitis C treatment on patient outcomes and healthcare costs. In summary, clinicians and healthcare decision-makers need data on the essential needs of their patient populations that are not being addressed using older therapies. Retrospective data analysis is used to generate essential need data, but no one study provides a complete picture of how well older products are meeting patient needs. Data limitations frequently complicate efforts of researchers to provide needed analyses, as was the case in Tandon et al. where missing data on genotype made defining an adequate duration of therapy impossible. The future is bright as electronic medical records data, such as that available through the Veterans’ Administration3, can clarify and simplify these analyses if used appropriately.
Declaration of interest The author reports no conflict of interest. The author alone is responsible for the content and writing of this article.
References 1. Tandon N, Balart L, Liliberte F, et al. Impact of completing chronic hepatitis (DHD) treatment on post-therapy healthcare costs. J Med Econ 2014;17: 862-71 2. McCombs JS, Shin J, Hines P, et al. Impact of drug therapy adherence in patients with hepatitis C. Am J Pharm Benefits 2012;4[Special Issue]: SP19-SP27 3. McCombs J, Matsuda T, Tonnu-Mihara I, et al. The risk of long-term morbidity and mortality in patients with chronic hepatitis C: results from an analysis of data from a Department of Veterans Affairs Clinical Registry. JAMA Intern Med. 2014;174:204-12
Letter to the editor McCombs
513
ISSN: 1369-6998 (Print) 1941-837X (Online) Journal homepage: http://www.tandfonline.com/loi/ijme20
Essential need for research in hepatitis C J. S. McCombs To cite this article: J. S. McCombs (2015) Essential need for research in hepatitis C, Journal of Medical Economics, 18:7, 512-513 To link to this article: http://dx.doi.org/10.3111/13696998.2015.1017502
Accepted author version posted online: 17 Feb 2015. Published online: 05 Mar 2015. Submit your article to this journal
Article views: 48
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ijme20 Download by: [University of Lethbridge]
Date: 14 November 2015, At: 12:18
Journal of Medical Economics 1369-6998 doi:10.3111/13696998.2015.1017502
Vol. 18, No. 7, 2015, 512–513
Article 0121.R1/1017502 All rights reserved: reproduction in whole or part not permitted
Letter to the editor Essential need for research in hepatitis C
Downloaded by [University of Lethbridge] at 12:18 14 November 2015
J. S. McCombs Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, School of Pharmacy, Leonard Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, USA
Letter in response to: Tandon N, Balart LA, Laliberte´ F, Pilon D, Lefebvre P, Germain G, Prabhakar A. Impact of completing chronic hepatitis C (CHC) treatment on posttherapy healthcare cost. J Med Econ 2014;17:862–71 Dear Editor,
Address for correspondence: J. S. McCombs, PhD, Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, School of Pharmacy, Leonard Schaeffer Center for Health Policy and Economics, University of Southern California, 635 Downey Way, VPD 212B Los Angeles, CA 90089-3333, USA. [email protected] Accepted: 6 February 2015; published online: 5 March 2015 Citation: J Med Econ 2015; 18:512–3
512
Letter to the editor McCombs
Clinicians and policy-makers need evidence with which to make decisions on how to most effectively employ new technologies as they are introduced into clinical practice. This need for cost-effectiveness information is particularly acute when a new treatment competes with older, effective alternatives. The cost-effectiveness of a new technology depends critically on the effectiveness of older therapies in meeting the clinical needs of real world patients. These ‘essential need’ data are obtained using data from paid claims or electronic medical records systems, analyzed using observational research designs, and multivariate statistical techniques. Additional data on incidence, prevalence, and costof-illness are generally available in the literature, as are studies estimating the impact of the disease state on the patient’s quality-of-life and work productivity. At product launch, these diverse sources of essential need information are combined with safety and efficacy data derived from the randomized clinical trials (RCTs) into computerized cost-effectiveness models which attempt to predict the fiscal and clinical impact of the new technology. Once a product is launched, clinicians and policy-makers need information on how well the new technology is meeting the essential need documented prior to product launch; however, it is uncommon that a technology innovator will conduct an ongoing program of research to document if their new technology performed as projected in their CEA models. That is, the innovator company generally leaves this task of evaluating the real-world impact of their technology to large ‘buyers’, such as HMOs. Unfortunately, the results of these internal studies by HMOs and other buyers may not be published. The task of documenting how well a new technology met the essential need at its launch will be eventually taken up by second-to-market companies in an effort to document the essential need for their competing products under development; however, it is common that a cluster of newer treatments will be launched in a span of a few years, which limits the ability of second-to-market companies to document the effectiveness of the innovator product due to lack of available data. This is the case in hepatitis C, and the paper by Tandon et al.1 must be evaluated in this context. Tandon et al. focus their analysis on the evaluation of ‘the healthcare resource utilization and cost alleviation associated with completing a 36–48 week HCV therapy using all of the currently available data from a large national healthcare claims database’ (p. 863, col. 1). To accomplish this objective, the authors use claims data from OptumHealth Reporting and Insights Database for January www.informahealthcare.com/jme ! 2015 Informa UK Ltd
Downloaded by [University of Lethbridge] at 12:18 14 November 2015
Journal of Medical Economics
2001 through to March 2013. These data include medical and pharmacy claims of more than 13 million privatelyinsured individuals covered by 60 self-insured Fortune 500 companies with locations in all census areas of the US. The data from OptumHealth do not include data on viral load or viral genotype that are critical for determining when an adequate course of therapy for hepatitis C has been achieved. Clinical studies have documented that patients with genotypes 2 and 3 require 28 weeks of interferon/ribavirin treatment compared to the 48 weeks required to effectively treat genotype 1 and 4. In response to these limitations in data, Tandon et al. excluded patients with 20–28 weeks of uninterrupted interferon therapy in an effort to remove patients with genotype 2 or 3 who may have completed an adequate course of HCV therapy. This step was necessary to remove these ‘completers’ from the population of patients who ‘fail’ treatment achieving less the 36 weeks of uninterrupted therapy. Tandon et al. elected to define an observation period for measuring costs that ‘spanned from 48 weeks after the index date to the earliest date between the end of insurance coverage or to the end of data availability’ (p. 863, col. 2). Stated bluntly, Tandon et al. ignore all cost of treatment incurred during the first 48 weeks, even as they use data from this period to calculate duration of therapy. While it is comforting to see that costs in the period following treatment have been diminished for patients who achieve 36–48 weeks of therapy, data on the costs associated with drug therapy adherence are needed to determine how the cost of the completed treatment compare to the saving attributed. An earlier paper2 documented that first year treatment cost increased significantly, primarily due to increased drug costs ($10,617 for completers of 24–48 weeks of therapy; $27,116 for patients with 48þ weeks of therapy). In the second year, total costs were reduced by $6171 and $8130, respectively. These latter results are consistent with the findings by Tandon et al.1. There are a few oddities reported in Tandon et al. that indicate potential confounding in this study’s attempt to measure the impact of 48þ weeks of continuous therapy on post-treatment costs. Tandon et al. report data for the proportion of days covered in Table 2—a well-accepted measure of adherence and patients who completed 48þ weeks of therapy have a mean ‘exposure to therapy’ of 489 days; however, patients defined as non-completers average 391 days of exposure. This may indicate that many non-compliers likely started and stopped therapy multiple times. This significant exposure to treatment by non-completers is likely to reduce their costs, thus narrowing the differences in costs over the post-treatment period. A sensitivity
! 2015 Informa UK Ltd www.informahealthcare.com/jme
Volume 18, Number 7
July 2015
analysis should have been investigated using PDC to define adherence, which would have provided a second measure of the impact of treatment on future costs. It is unfortunate that researchers bypass opportunities to document additional and important parameters of ‘essential need’, despite having access to adequate data. For example, in Tandon et al., only 5004 of 30,307 patients with a diagnosis of CHC initiated interferon treatment (16.5%). This treatment rate exceeds the 5.3% treatment rate reported based on an analysis of a commercial database2, but is lower than the 24.3% treatment rate recently reported for VA patients with a detectable viral load3. While these reported treatment rates are low, these three studies do not provide any analysis on the predictors of treatment initiation or treatment response, such as achieving an undetectable viral load. This leaves a gap in the data needed to accurately project the potential impact of the new hepatitis C treatment on patient outcomes and healthcare costs. In summary, clinicians and healthcare decision-makers need data on the essential needs of their patient populations that are not being addressed using older therapies. Retrospective data analysis is used to generate essential need data, but no one study provides a complete picture of how well older products are meeting patient needs. Data limitations frequently complicate efforts of researchers to provide needed analyses, as was the case in Tandon et al. where missing data on genotype made defining an adequate duration of therapy impossible. The future is bright as electronic medical records data, such as that available through the Veterans’ Administration3, can clarify and simplify these analyses if used appropriately.
Declaration of interest The author reports no conflict of interest. The author alone is responsible for the content and writing of this article.
References 1. Tandon N, Balart L, Liliberte F, et al. Impact of completing chronic hepatitis (DHD) treatment on post-therapy healthcare costs. J Med Econ 2014;17: 862-71 2. McCombs JS, Shin J, Hines P, et al. Impact of drug therapy adherence in patients with hepatitis C. Am J Pharm Benefits 2012;4[Special Issue]: SP19-SP27 3. McCombs J, Matsuda T, Tonnu-Mihara I, et al. The risk of long-term morbidity and mortality in patients with chronic hepatitis C: results from an analysis of data from a Department of Veterans Affairs Clinical Registry. JAMA Intern Med. 2014;174:204-12
Letter to the editor McCombs
513
