Use of real-world evidence in pharmacoeconomic analysis
Huang, Yunyu
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Use of Real-World Evidence in
Pharmacoeconomic Analysis
Illustrations for The Netherlands and China
Use of Real-World Evidence in Pharmacoeconomic Analysis: Illustrations for The Netherlands and China
PhD dissertation, University of Groningen, University Medical Center Groningen, the Netherlands
The work presented in this thesis results from a joint Doctorate between the University of Groningen, University Medical Center Groningen, and Fudan University (Shanghai, China).
Financial support for the publication of this thesis was partially provided by the University of Groningen, University Medical Center Groningen, and Research Institute SHARE (Science in Healthy Ageing & healthcaRE).
ISBN: 978-94-034-1870-4 (printed version)
978-94-034-1869-8 (digital version)
Cover design and layout: Eduard Boxem | www.persoonlijkproefschrift.nl Printing: Ridderprint BV | www.ridderprint.nl
© 2019, Yunyu Huang, Groningen, the Netherlands
All rights reserved. No parts of this thesis may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system, without permission of the author.
Pharmacoeconomic Analysis
Illustrations for The Netherlands and China
PhD thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the Rector Magnificus Prof. C. Wijmenga
and in accordance with the decision by the College of Deans. This thesis will be defended in public on Monday 16 September 2019 at 11:00 hours
by
Yunyu Huang
born on 29 August 1985 in Shanghai, China
Prof. F.M. Haaijer-Ruskamp Prof. M.J. Postma Co-supervisor Dr. P. Vemer Assessment committee Prof. K. Taxis
Prof. S.M.A.A. Evers Prof. H.V. Hogerzeil
Dr. J.F.M. van Boven Dr. H. Wang
Chapter 1 Introduction 9 Chapter 2 Economic evaluations of angiotensin-converting enzyme
inhibitors and angiotensin II receptor blockers in type 2 diabetic nephropathy: a systematic review
21
Chapter 3 Using primary care electronic health record data for comparative effectiveness research: experience of data quality assessment and preprocessing in The Netherlands
61
Chapter 4 Comparing the effect of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers on renal function decline in diabetes
81
Chapter 5 Economic burden in Chinese patients with diabetes mellitus using electronic insurance claims data
105 Chapter 6 Cost-effectiveness and budget impact analysis of
improvement of medication utilization in patients with type 2 diabetes and nephropathy from a healthcare payer perspective in China
127
Chapter 7 General discussion and future perspectives 155
Summary 168
Appendices
Nederlandse samenvatting 172
Acknowledgments 176
12
The goal of health economics (HE) is to answer the question of how to allocate scarce resources to improve health [1]. Outcomes research (OR) is to evaluate the clinical outcomes of health care interventions. The integrative area of health economics and outcomes research (HEOR) helps healthcare decision makers to decide which health interventions to invest in based on patients’ needs, safety, efficacy and effectiveness of health interventions, cost-effectiveness and budget impact. HEOR is becoming increasingly important as a tool for assessing new health interventions, especially for pharmaceuticals, throughout the world. Challenges still exist for gathering HEOR evidence to support decision-making, e.g. how to interpret clinical efficacy and real-world benefits, the topic of this thesis.
PHARMACOECONOMICS AND DATA SOURCES
As HE research specifically directed at pharmaceuticals, pharmacoeconomics (PE) compares the cost and efficacy/effectiveness of pharmaceutical products to guide the use of scarce resources to achieve best value to patients, healthcare payers and society [2, 3]. Evidence of value for money is increasingly desired along with clinical efficacy/ effectiveness by different payers and healthcare systems [4], enhancing the further development of PE.
Although a variety of tools and techniques for conducting PE analyses have been developed, it is commonly recognized that there are two competing approaches to the economic evaluation of pharmaceuticals. In a trial-based study, outcomes and costs data are collected concurrently with the clinical trial. In a decision-model-based study, data from a number of sources are synthesized [5].
Whether PE analyses are trial-based or modeling studies, the overall data requirements are the same: it is about comparative effectiveness, resources, costs and health benefits (Table 1 [5]).
Table 1. Data required in PE
Required data Data source
Comparative treatment efficacy/effectiveness
(e.g., clinical outcomes, survival) RCTs, observational studies or systematic literature review / meta analyses
Consumption of healthcare resources Obtained concurrently during RCTs or
observational data such as electronic health records, claims data, local surveys, patient-chart reviews or expert opinions
Costs of resources Observational data such as claims data from
payers or other routine data collection
Health state preference / utility During RCTs, specific utility studies, or from
surveys of the general population
PE, pharmacoecnomics; RCT, randomized controlled trials.
First, data defining comparative treatment efficacy/effectiveness, e.g. clinical outcomes or life-years, are required. Often these come from randomized controlled trials (RCTs), and can be used directly in the economic study. However, in the modelling studies, the effect data could be derived from not only RCTs but also observational sources (e.g., claims databases, epidemiologic studies). RCTs compare the efficacy and safety of a medication vs. a comparator, using pre-defined clinical endpoints and in a controlled environment. In contrast, observational studies usually adopt a cohort or case-control design in a more general clinical practice setting [6]. Despite limitations of ‘external validity’, RCTs remain the main data source at a higher evidence hierarchy level to evaluate the treatment effects in PE, especially for new drugs. But clinical outcomes derived from RCTs are often not valid for real world. In recent years, studies based on observational effectiveness data were published to reflect the value of real-world evidence in PE [7-10].
In order to calculate the total cost or incremental cost of one treatment over another, data describing the quantities of resources consumed by the treatments being compared and the unit costs or prices of resources are required. Based on different perspectives, a range of resources could be used in PE studies [11]. These include drugs, medical care, or other patient family’s own resources, such as traveling to healthcare facilities or providing nursing support at home. These data can either be collected alongside a trial or be derived from a range of other observational sources, including electronic health records (EHRs), claims data, local surveys, and expert opinions.
Finally, data of patients’ preference on health states may be required, especially if the aim of the PE study is to calculate the cost per quality-adjusted life year (QALY) gained. Such
14
data can also be collected by the survey alongside a trial, or by asking patients to value their own current health states using standard-gamble or time-tradeoff methods, or by surveys in members of the general public [5]. Also, patient-reported outcomes are often combined with valuations derived from the general public [12].
OBSERVATIONAL DATA IN PE
Pros and cons of observational data
Although RCTs are the gold standard to determine a drug’s efficacy, it is well recognized that results of RCTs may not sufficiently reflect effectiveness of therapies delivered in real world [13, 14]. For decision-making in PE, policy makers require the best available evidence. Routinely collected and electronically stored healthcare data in routine clinical practice has been widely developed and utilized over the past decades [15]. There is increasing awareness that observational studies can complement RCTs to examine efficacy findings from RCTs in larger patient populations in real-world settings.
Compared with RCTs, observational studies are usually not limited by strict patient selection criteria, which may enhances their external validity [16]. For example, patients with serious comorbidities are often excluded from RCTs but can be included in observational studies.
However, observational studies may have the limitation of lower internal validity due to the potential for selection bias and the wide range of confounders [5, 17]. Unless the presence of those biases and confounders can be minimized and/or corrected for, the estimated treatment effectiveness from observational studies may not necessarily always imply a valid cause and effect relationship [18].
Use of observational data
If the impact of bias and confounders can be precisely estimated and minimized in observational studies, observational studies may contribute to providing informative real-world evidence to PE.
For example, cost-effectiveness of a treatment may vary over time, which can be observed in a real-world setting. In these cases, observational studies can be performed to compare the results from a RCT with the outcomes of the same treatment after it is in general use. These comparisons may show the differences between efficacy of a treatment under strict assessment conditions and the effectiveness seen during actual use [19]. Such information
can help decision makers to potentially adjust their decision on the reimbursement of a treatment. For example, in the Netherlands, the Dutch authorities previously determined that data on the actual effectiveness of a new drug, rather than efficacy, needed to be collected in an outcomes study during a period of conditional reimbursement, to better understand what the cost-effectiveness of the drug will be in daily practice [20]. Of note, in the meantime the conditional reimbursement system in the Netherlands has already been transformed again.
In addition, with the development of PE worldwide, it has become evident that economic evaluations are not generalizable between countries [21, 22]. This is mainly attributable to differences in patients’ characteristics, healthcare resource utilization, clinical practice, and costs. This also introduces a particular problem in trial-based economic evaluations where efficacy of the treatment and resource use are evaluated across study sites, as the result data may not be applicable to a specific location. In these cases, evidence from observational studies may help to bridge the gap for the translation of results from RCTs to country-specific settings. For example, information with respect to resource use and costs as well as prevalence of illness severity and patient comorbidity need to be collected from local observational data sources to reflect the actual situation of local patient populations.
Methods for observational data analysis
The reason for the reluctance to use observational data by healthcare decision makers was mainly due to the uncertainty about the reliability and robustness of results derived from observational studies. Three main issues related to using observational data were usually encountered, in particular confounding by indication, missing data, and insufficient numbers of comparable patients [23]. To include sufficient patient population for observational studies, different organizations have put efforts in building large-scale healthcare data sources, e.g. databases representing integrated healthcare-delivery networks. Infrastructures have also been developed to ensure evidence can be generated more transparently from those large-scale databases [24]. For the other two issues, a number of analytical methods have been developed over the years. Those analytical approaches include matching, regression analysis, propensity scores, instrumental variables, difference-in-differences approach, and control functions to minimize selection bias [18], and multiple imputation methods to deal with missing data which are missing at random.
16
Although healthcare policymakers, payers, and providers are increasingly turning to those observational analyses to answer a variety of questions, assessment of the comparative treatment effectiveness through the analysis of secondary data sources is still controversial due to the study design and data quality. Matching methods can act as an important pre-processing method on the data before concluding the comparative effectiveness in observational studies. This approach allows the analyst to consider problems of imbalance in covariates and assess overlap between treatment groups before and after matching [25]. Yet, improvements of matching methods in economic studies are still required. A review from 2013 showed that economic evaluations using matching methods often didn’t report details of the matching [18]. For example, solutions relying on the propensity score require post-match balance in the entire distribution of individual covariates.
PE IN DIABETES AND NEPHROPATHY
The rapid growth of non-communicable diseases represents an enormous disease burden on different healthcare systems around the world. The worldwide incidence of diabetes and especially the diabetes-related complications highlight the relevant economic burden of the disease [26]. Among those complications, diabetic nephropathy occurs in up to 40% of diabetic patients with microalbuminuria, and is the major cause of end-stage renal disease (ESRD) in many regions of the world [27, 28].
The high costs of diabetes and ESRD clearly put forward the need for improved prevention strategies. Effective allocation of resources remains a significant challenge to many healthcare systems given the need for long-term planning of resources, the emerging of new drugs and technologies, and the benefits vs. costs of implementing new prevention strategies [29]. PE is increasingly used to inform decision makers about the value for money of alternative treatment strategies in diabetes and diabetic nephropathy [30].
Observational data are likely to be particularly valuable for formulating appropriate diabetes treatment pathways and improving patient outcomes, since the long-term outcomes may only be reflected in the real-world setting and the resources use pattern may be distinct from RCTs. For those countries without available national-specific RCT data, studies using the observational data such as EHRs may act as an important role for PE studies.
In low- and middle-income countries, the high economic burden of diabetes and diabetic nephropathy is usually compounded with inadequate resource use for diabetes care when compared with high-income countries. In addition, there is usually a lack of local cost data is in these countries [31]. As a result, studies evaluating diabetes costs in these countries are relatively scarce. PE studies with local observational cost data can be highly valuable for decision makers to develop country-specific strategies with high value for money.
AIMS AND OUTLINE OF THE THESIS
In this thesis, several aspects related to the use of observational data in PE studies for diabetic nephropathy will be analyzed and discussed. The main objective of the thesis is to assess the added value of use of observational data in PE. The thesis has five sub-aims: 1) to synthesize the information from existing PE studies evaluating angiotensin-converting enzyme inhibitors (ACE inhibitors) and angiotensin II receptor blockers (ARBs) in patients with type 2 diabetes (T2D) and nephropathy (Chapter 2);
2) to explore the methods for improving quality of observational data (Chapter 3); 3) to assess comparative effectiveness of ACE inhibitors and ARBs on renal function decline
in patients with T2D and nephropathy using observational data (Chapter 4)
4) to assess the development of disease burden of diabetes in China using observational data (Chapter 5);
5) to explore the cost-effectiveness and budget impact of ACE inhibitors and ARBs in Chinese patients with T2D and nephropathy (Chapter 6)
We start with a systematic review of the economic evaluation of ACE inhibitors and ARBs in patients with T2D and nephropathy in Chapter 2. Treatment guidelines recommend ACE inhibitors and ARBs as the first-choice agents for treating diabetic nephropathy. Although different PE evaluations of ACE inhibitors and ARBs have been published based on RCT results, a systematic PE comparison of ACE inhibitors and ARBs in patients with T2D and nephropathy is still lacking. In this chapter, the similarities and differences in cost-effectiveness analyses for ACE inhibitors and ARBs in patients with diabetic nephropathy are addressed.
In Chapter 3, we turn to the issue of data quality in observational data sources. In recent years, the increasing availability of EHRs provides opportunities to study drug use patterns
18
or drug effectiveness in comparative effectiveness research. This has resulted in a request of high-quality data and data processing approaches to ensure the validity of comparative effectiveness research. Details of data quality and how quality issues were solved have scarcely been reported in published comparative effectiveness studies. In this chapter we apply a general framework to illustrate the problems and solutions of data quality assessment and pre-processing.
In Chapter 4, a comparative effectiveness study is performed to evaluate drugs’ effectiveness using observational data in the Netherlands, still focusing on ACE inhibitors and ARBs. Due to the lack of head-to-head comparisons between the ACE inhibitors and ARBs for protecting patients from renal function decline in an unselected T2D population, this study is aimed to compare effectiveness of ACE inhibitors and ARBs on nephropathy in T2D patients in primary care. Data quality pre-processing as illustrated in Chapter 3 is applied to improve the internal validity of the results.
In Chapter 5, we choose China as example to show the use of observational data in cost evaluation in a middle-income country. Although the Chinese Diabetes Society has published a guideline of prevention and treatment for T2D, the guideline is often not followed in real-world treatment. This may lead to non-optimal control of the course of disease compared with high-income countries. This chapter describes the longitudinal development of the economic burden of diabetes in urban China using electronic claims data. The cost analysis using valid observational data could help to improve PE evaluation, disease management and reimbursement policy-making in China.
In Chapter 6, a cost-effectiveness and budget impact analysis is performed to evaluate the use of ACE inhibitors and ARBs in Chinese patients with T2D and nephropathy. Evidence from Chapter 5 showed that the use of ACE inhibitor/ARB was not optimal in patients with T2D in China. With the increasing prevalence of diabetes and its complications, the financing of Chinese urban basic health insurance system will face more challenges if the treatment strategies are not optimized. This chapter performs a modeling analysis to evaluate the cost-effectiveness of ACE inhibitor/ARB or other active drugs vs. no treatment. Budget impact analysis is also performed to evaluate how optimal use of ACE inhibitor/ARB in Chinese patients with diabetes may influence the health insurance financing.
We finish with a discussion in Chapter 7 of the research presented in this thesis regarding the use of observational data in PE.
REFERENCES
1. Wang, B.C., The rise of health economics and outcomes research in healthcare decision-making. Leuk Res, 2013. 37(3): p. 238-9.
2. Drummond, M., et al., Use of pharmacoeconomics information--report of the ISPOR Task Force on use
of pharmacoeconomic/health economic information in health-care decision making. Value Health,
2003. 6(4): p. 407-16.
3. Arenas-Guzman, R., et al., Pharmacoeconomics--an aid to better decision-making. J Eur Acad Dermatol Venereol, 2005. 19 Suppl 1: p. 34-9.
4. O’Sullivan, A.K., D. Thompson, and M.F. Drummond, Collection of health-economic data alongside
clinical trials: is there a future for piggyback evaluations? Value Health, 2005. 8(1): p. 67-79.
5. Drummond, M.F., Experimental versus observational data in the economic evaluation of
pharmaceuticals. Med Decis Making, 1998. 18(2 Suppl): p. S12-8.
6. Silverman, S.L., From randomized controlled trials to observational studies. Am J Med, 2009. 122(2): p. 114-20.
7. Mullol, J., et al., Pharmacoeconomics of Cyclamen europaeum in the management of acute
rhinosinusitis. Laryngoscope, 2013. 123(11): p. 2620-5.
8. van Gils, C.W., et al., Real-world cost-effectiveness of oxaliplatin in stage III colon cancer: a synthesis
of clinical trial and daily practice evidence. Pharmacoeconomics, 2013. 31(8): p. 703-18.
9. Maoujoud, O., S. Ahid, and Y. Cherrah, The cost-utility of treating anemia with continuous
erythropoietin receptor activator or Epoetin versus routine blood transfusions among chronic hemodialysis patients. Int J Nephrol Renovasc Dis, 2016. 9: p. 35-43.
10. Tanaka, E., et al., Pharmacoeconomic analysis of biological disease modifying antirheumatic drugs
in patients with rheumatoid arthritis based on real-world data from the IORRA observational cohort study in Japan. Mod Rheumatol, 2016: p. 1-10.
11. Husereau, D., et al., Consolidated Health Economic Evaluation Reporting Standards
(CHEERS)--explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. Value Health, 2013. 16(2): p. 231-50.
12. Krabbe, P., The Measurement of Health and Health Status: Concepts, Methods and Applications from
a Multidisciplinary Perspective. 2016: Academic Press.
13. Concato, J., N. Shah, and R.I. Horwitz, Randomized, controlled trials, observational studies, and the
hierarchy of research designs. N Engl J Med, 2000. 342(25): p. 1887-92.
14. Concato, J., Observational versus experimental studies: what’s the evidence for a hierarchy? NeuroRx, 2004. 1(3): p. 341-7.
20
15. Berger, M.L., et al., Good research practices for comparative effectiveness research: defining,
reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--Part I.
Value Health, 2009. 12(8): p. 1044-52.
16. Freemantle, N. and F. Hessel, The applicability and generalizability of findings from clinical trials
for health-policy decisions. Pharmacoeconomics, 2009. 27(1): p. 5-10.
17. Reeves, B.C., Limitations of analyses of effectiveness using observational data. Eur Heart J, 2006.
27(14): p. 1642-3.
18. Rovithis, D., Do health economic evaluations using observational data provide reliable assessment
of treatment effects? Health Econ Rev, 2013. 3(1): p. 21.
19. Baltussen, R., R. Leidl, and A. Ament, Real world designs in economic evaluation. Bridging the gap
between clinical research and policy-making. Pharmacoeconomics, 1999. 16(5 Pt 1): p. 449-58.
20. Ramos, I.C., et al., Determining the impact of modeling additional sources of uncertainty in
value-of-information analysis. Value Health, 2015. 18(1): p. 100-9.
21. Sculpher, M.J., et al., Generalisability in economic evaluation studies in healthcare: a review and case
studies. Health Technol Assess, 2004. 8(49): p. iii-iv, 1-192.
22. Drummond, M., A. Manca, and M. Sculpher, Increasing the generalizability of economic evaluations:
recommendations for the design, analysis, and reporting of studies. Int J Technol Assess Health Care,
2005. 21(2): p. 165-71.
23. Blommestein, H.M., M.G. Franken, and C.A. Uyl-de Groot, A practical guide for using registry data to
inform decisions about the cost effectiveness of new cancer drugs: lessons learned from the PHAROS registry. Pharmacoeconomics, 2015. 33(6): p. 551-60.
24. Wang, S.V., et al., Transparency and Reproducibility of Observational Cohort Studies Using Large
Healthcare Databases. Clin Pharmacol Ther, 2016. 99(3): p. 325-32.
25. Stuart, E.A., Matching methods for causal inference: A review and a look forward. Stat Sci, 2010.
25(1): p. 1-21.
26. Giannini, C., A. Mohn, and F. Chiarelli, Technology and the issue of cost/benefit in diabetes. Diabetes Metab Res Rev, 2009. 25 Suppl 1: p. S34-44.
27. Vora, J.P., H.A. Ibrahim, and G.L. Bakris, Responding to the challenge of diabetic nephropathy: the
historic evolution of detection, prevention and management. J Hum Hypertens, 2000. 14(10-11): p.
667-85.
28. Schiffrin, E.L., M.L. Lipman, and J.F. Mann, Chronic kidney disease: effects on the cardiovascular
system. Circulation, 2007. 116(1): p. 85-97.
29. Bottomley, J.M. and F.D. Raymond, Pharmaco-economic issues for diabetes therapy. Best Pract Res Clin Endocrinol Metab, 2007. 21(4): p. 657-85.
30. Huang, Y., et al., Economic evaluations of angiotensin-converting enzyme inhibitors and angiotensin
II receptor blockers in type 2 diabetic nephropathy: a systematic review. BMC Nephrol, 2014. 15: p.
15.
31. Baik, S., et al., Conducting cost-effectiveness analyses of type 2 diabetes in low- and middle-income
countries: can locally generated observational study data overcome methodological limitations?
Diabetes Res Clin Pract, 2010. 88 Suppl 1: p. S17-22.
2
Economic evaluations of
angiotensin-converting enzyme inhibitors and
angiotensin II receptor blockers
in type 2 diabetic nephropathy:
a systematic review
Yunyu Huang*
1,2,3Qiyun Zhou
1Flora M. Haaijer-Ruskamp
2Maarten J. Postma
11Department of Pharmacy, Unit of Pharmaco Epidemiology & Pharmaco Economics,
University of Groningen, Groningen, The Netherlands
2Department of Clinical Pharmacology, University Medical Center Groningen,
University of Groningen, Groningen, The Netherlands
3School of Public Health, Fudan University, Shanghai, China
*Corresponding author Published in BMC Nephrol. 2014;15:15.
24
ABSTRACT
Background:
Structured comparison of pharmacoeconomic analyses for ACEIs and ARBs in patients with type 2 diabetic nephropathy is still lacking. This review aims to systematically review the cost-effectiveness of both ACEIs and ARBs in type 2 diabetic patients with nephropathy.
Methods:
A systematic literature search was performed in MEDLINE and EMBASE for the period from November 1, 1999 to Oct 31, 2011. Two reviewers independently assessed the quality of the articles included and extracted data. All cost-effectiveness results were converted to 2011 Euros.
Results:
Up to October 2011, 434 articles were identified. After full-text checking and quality assessment, 30 articles were finally included in this review involving 39 study settings. All 6 ACEIs studies were literature-based evaluations which synthesized data from different sources. Other 33 studies were directed at ARBs and were designed based on specific trials. The Markov model was the most common decision analytic method used in the evaluations. From the cost-effectiveness results, 37 out of 39 studies indicated either ACEIs or ARBs were cost-saving comparing with placebo/conventional treatment, such as amlodipine. A lack of evidence was assessed for valid direct comparison of cost-effectiveness between ACEIs and ARBs.
Conclusion:
There is a lack of direct comparisons of ACEIs and ARBs in existing economic evaluations. Considering the current evidence, both ACEIs and ARBs are likely cost-saving comparing with conventional therapy, excluding such RAAS inhibitors.
BACKGROUND
Approximately one fourth to one third of patients with diabetes mellitus develop renal manifestations [1-4]. Clinical stages of diabetic nephropathy are generally categorized into stages based on the values of urinary albumin excretion: microalbuminuria (MiA) and macroalbuminuria (MaA) [5]. The prevalence of MiA and MaA in type 2 diabetes is as high as 37–40% in western countries and 57.4–59.8% in Asian countries [6-8]. 20–40% of type 2 diabetic patients with MiA progress to overt nephropathy, and by 20 years after onset of overt nephropathy, about 20% will have progressed to end-stage renal diseases (ESRD) [9]. Because of the large prevalence, diabetes has become the most common single cause of ESRD in the U.S. and Europe [10,11]. As therapies and interventions for coronary artery disease continue to improve, more patients with type 2 diabetes may be expected to survive long enough to develop renal failure.
In developed countries, ESRD is a major cost driver for health-care systems, with annual growth of dialysis programs ranging between 6% and 12% over the past two decades and continuing to grow, particularly in developing countries [12]. Although there are no definitive cure solutions, there is good evidence that adequate treatment can delay or prevent the progress of diabetic nephropathy including strict control of glycaemia, early treatment of hypertension, dietary protein restriction and lipid-lowering therapy [13]. Targeting renin–angiotensin–aldosterone system (RAAS) is the most effective way to delay renal disease progression. Treatment guidelines therefore recommended angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) as the first-choice agents for treating nephropathy in diabetic patients [14].
Both ACEIs and ARBs target the RAAS and have proven their renal protective effects in diabetic patients in various clinical trials. One disadvantage of ACEIs [15-17] in comparison with ARBs is the higher risk of dry cough while significant differences in effectiveness between these two drug classes have not been shown convincingly although ARBs have been more thoroughly investigated in controlled settings in the recent decade providing relatively high levels of evidence. Often clinical practice guidelines recommend both ACEIs and ARBs in diabetic patients with or even without (micro)albuminuria [18].
Pharmacoeconomic evaluations of ACEIs and ARBs have been widely applied based on clinical trials’ results. The pharmacoeconomic results of ARBs have been reviewed previously [19-26]. ARBs were suggested to be cost saving in type 2 diabetic patients with nephropathy versus conventional therapy, largely due to the high costs of treatment
26
of ESRD. However, a systematic review of cost-effectiveness results of ACEIs in type 2 diabetic patients with renal disease is still lacking. In addition, the need of a structured pharmacoeconomic comparison of the ACEIs with ARBs is pointed out by some researchers [21,26].
The aim of this study is to address the similarities and differences in cost-effectiveness analyses for both ACEIs and ARBs in type 2 diabetic patients with nephropathy. In particular, three objectives are addressed: 1) to summarize the cost-effectiveness of ACEIs; 2) to update the cost-effectiveness of ARBs; 3) to compare the characteristics of different economic evaluations and analyze potential differences and similarities in the cost-effectiveness between the two drug classes reviewed.
METHODS
Literature search strategy
A systematic literature search was performed in MEDLINE and EMBASE for the period November 1, 1999 to Oct 31, 2011. The key words (MeSH headings in MEDLINE, EMtree terms in EMBASE and other text terms) included were (Table 1):
– Indicating target drugs, the variations in and abbreviations of ACEIs and ARBs were searched, such as ‘angiotensin receptor antagonists’, ‘renin angiotensin aldosterone system inhibitors’, and specific drug names of different ACEIs or ARBs, including 10 specific ACEIs (such as captopril, enalapril, etc.) and 8 ARBs (such as losartan, irbesartan, etc.).
– Indicating diabetic nephropathy, key words were limited to ‘type 2 diabetes’ and its variations. Variations of nephropathy were combined with diabetes, such as ‘diabetic renal diseases’ or ‘diabetic kidney diseases’.
– Indicating economic evaluations, various key words relating to different evaluation types, pharmacoeconomics, cost of drugs and cost analysis were searched, including ‘cost-effectiveness analysis’ (CEA), ‘cost-utility analysis’ (CUA), ‘cost-benefit analysis’ (CBA), and ‘cost savings’, etc.
The references of identified articles were manually screened for relevant economic evaluations not identified in the above-mentioned searches (snowballing).
Table 1. Search terms for systematic review
Search terms MEDLINE EMBASE
Drug Mesh: Angiotensin-Converting
Enzyme Inhibitors;
Angiotensin Receptor Antagonists;
TIAB (Title and Abstract):
ACEIs; ARBs; ACEI; ARB; renin angiotensin system inhibitor*a;
renin angiotensin aldosterone system inhibitor*; ACE inhibitor*; RAS inhibitor*; RAAS inhibitor*; angiotensin converting enzyme inhibitor*; renin angiotensin system inhibitor*; angiotensin receptor blocker*; Losartan; Candesartan; Valsartan; Irbesartan; Telmisartan; Eprosartan; Olmesartan; Azilsartan; Benazepril; Captopril; Enalapril; Fosinopril; Lisinopril; Moexipril; Perindopril; Quinapril; Ramipril; Trandolapril
EMtree: dipeptidyl carboxypeptidase
inhibito; angiotensin receptor antagonist;
ab,ti (Abstract and Title): angiotensin
receptor blocker; angiotensin receptor blockers; arb; arbs; ace inhibitor; ace inhibitors; angiotensin converting enzyme inhibitor; angiotensin converting enzyme inhibitors; angiotensin converting enzyme (ace) inhibitor; angiotensin converting enzyme (ace) inhibitors; acei; aceis; renin angiotensin system inhibitor; renin angiotensin system inhibitors; renin angiotensin system (ras) inhibitor; renin angiotensin system (ras)
inhibitors; ras inhibitor; ras inhibitors; renin angiotensin aldosterone system inhibitor; renin angiotensin aldosterone system inhibitors; raas inhibitor; raas inhibitors; losartan; candesartan; valsartan; irbesartan; telmisartan; eprosartan; olmesartan; azilsartan; benazepril; captopril; enalapril; fosinopril; lisinopril; moexipril; perindopril; quinapril; ramipril; trandolapril;
Diabetic Nephropathy (DN)
Mesh: Diabetes Mellitus, Type 2;
Diabetic Nephropathies; Kidney Failure, Chronic;
TIAB: diabetic nephropathy*;
diabetic renal disease*; diabetic kidney disease*;
EMtree: non insulin dependent diabetes
mellitus; diabetic nephropathy;
ab,ti: diabetic nephropathy; diabetic
nephropathies; diabetic renal diseases; diabetic renal disease; diabetic kidney diseases; diabetic kidney disease
Economic Evaluation (EE)
Mesh: Economics, Pharmaceutical;
Costs and Cost Analysis; Drug Costs; Cost Savings; Cost of Illness; Cost-Benefit Analysis;
TIAB: cost effect*; cost utility; cost
benefit*; economic evaluation*; cost analys*
EMtree: pharmacoeconomics; economic
evaluation; drug cost; cost control; cost of illness; cost benefit analysis; cost effectiveness analysis;
ab,ti: cost effectiveness; cost utility; cost
benefit; economic evaluation; economic evaluations; cost analys;
28
Table 1. Continued
Search terms MEDLINE EMBASE
Search
Strategy ( “ Drug Term 1 ” [Mesh] OR “ Drug Term 2 ” [TIAB] … ) AND ( “ DN Term 1 ” [Mesh] OR “ DN Term 2 ” [TIAB] … ) AND ( “ EE Term 1 ” [Mesh] OR “ EE Term 2 ” [TIAB] … )
( ‘ Drug Term 1 ’ /exp OR ‘ Drug Term 2 ’ :ab,ti … ) AND ( ‘ DN Term 1 ’ /exp OR ‘ DN Term 2 ’ :ab,ti … ) AND ( ‘ EE Term 1 ’ /exp OR ‘ EE Term 2 ’ :ab,ti … ) NOT
[medline]/limb
a: An asterisk (*) following the word is the wildcard character, which means to search in MEDLINE for all terms that begin with a word; b: To exclude articles that can be found in MEDLINE.
Study selection
Inclusion criteria for the review were as follows (following the PICOS-design):
– Population: patients in studies had to have type 2 diabetes with symptoms of renal diseases;
– Interventions and Comparators: studies must examine an ACEI- or ARB-based treatment regimen for the progression of diabetic nephropathy compared with regimens that did not include these medications, or if available, compare ACEIs with ARBs directly;
– Outcomes: clinical outcomes should be relevant to renal disease symptoms, including overt diabetic nephropathy, ESRD (kidney transplantation or dialysis), all-cause mortality, etc.; and
– Study design: studies had to be original economic evaluations.
Other criteria concerned that studies had to have been published as full-length articles and were peer-reviewed for English-language journals.
Study selection was performed in three rounds. First, titles and abstracts of searched articles were scanned and checked. In the second round, the full-texts of included articles were read carefully and quality was assessed in the last round. Two authors independently assessed the quality of the articles included and extracted the data. Differences were resolved by consensus.
Quality assessment
Quality assessment was conducted at the ‘study’ level, i.e. each study was analyzed one by one. A checklist for critical appraisal of economic evaluations [27] was used to evaluate the study quality. The checklist comprises 12 criteria assessing the study design, outcomes
and costs and the extrapolation of the results of an economic evaluation. An additional file shows this checklist in more detail (see Additional file 1).
The criterion ‘applicable to local population’ was not included in the assessment as we didn’t felt this was relevant for the current study; i.e. 11 criteria were considered in the end. In case studies showing cost savings, the absence of an explicit incremental cost-effectiveness ratio (ICER) was classified as adequate, since in that case no incremental ratio is necessary or meaningful.
Studies were subsequently included in the full review if: 1) the outcomes and costs have been assessed as being credibly, 2) at least 6 of the 11 quality criteria were rated as adequate or good; and 3) not more than three quality criteria were assessed as being inadequate.
Data extraction
Data extraction was based on the 11 criteria included in the quality assessment checklist which concerned: 1) basic information of study design; 2) data on outcomes and costs; and 3) results and conclusions. We grouped articles into two groups, reflecting ACEIs and ARBs. The latter group was subdivided into three subgroups in line with the three mostly analyzed ARBs, irbesartan, losartan and valsartan.
To make the results comparable across the studies, cost-saving or ICER results were standardized to 2011 price levels, by applying the appropriate annual deflators for each country, based on the statistics from the World Bank [28]. Since the deflator data for Taiwan was not available from the World Bank, cost data of this region was not standardized. The original cost-saving result was showed as reference.
All the currencies were converted to 2011 Euros, based on the Euro rate as of June 30th, 2011 [29].
The results of selected studies were classified in 5 categories: 1) cost-saving: net life years or QALYs gained in conjunction with ≥ €1,000 saved per patient as compared with the comparison intervention; 2) almost cost-neutral: net life years or QALYs gained, with < €1,000 saved per patient; 3) very cost-effective: 0 < ICER ≤ €20,000; 4) cost-effective: €20,000 < ICERs ≤ €40,000; 5) not cost-effective: ICERs > €40,000. The classification was based on both literature and suggestions in identified studies in this review [30,31].
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RESULTS
Up to October 2011, 434 articles (141 articles from PubMed and 293 articles from EMBASE) were identified. After full-text checking, 32 articles were included into the quality assessment. After quality assessment, 30 articles were finally included in this review (Figure 1). One of the excluded articles had 4 criteria assessed as inadequate and only 4 criteria assessed as good. The other one merely got 5 criteria rated as adequate among the 11 criteria considered.
Among these 30 selected articles, in one article on losartan for an Asian population [32] only the data from Hong Kong were considered as the cost data from other Asian countries or regions assessed seemed not to be of adequate quality. Finally, 39 studies in different countries or regions contained in these 30 articles were included in the analysis.
Summary of selected studies
Table 2 summarizes the basic features of studies included. All six ACEIs studies [33-38] were literature-based evaluations which synthesized data from different sources. All ARBs studies [32,39-62] were designed based on specific trials. The Markov model was the most common decision analytic method used in these evaluations. From the cost-effectiveness results, 37 out of 39 studies indicated both ACEIs and ARBs were cost-saving comparing with placebo/conventional treatment or amlodipine. In the absence of clear cost savings, cost neutrality of very favorable cost-effectiveness was achieved minimally. No studies were identified with a direct cost-effectiveness comparison between ACEIs and ARBs. Table 2. Summary of selected studies (number of study)
ACEIs
(Total 6) ARBs(Total 33) ARBs Losartan (Total 14) ARBs Irbesartan (Total 18) ARBs Valsartan (Total 1)
Data source Trial based 0 33 14 18 1
Literature based 6 0 0 0 0 Intervention and control group Comparing with placebo / conventional therapy 2 22 14 8 0 Comparing with other drugs 0 12 0 11 1 Comparing different strategies 4 10 0 10 0 Decision
Model Markov modelWeibull model 60 203 13 180 10
Regression
method 0 10 10 0 0
Perspective Third party
payer 4 33 14 18 1 Societal 2 0 0 0 0 CE results Cost-saving 5 32 13 18 1 Cost-neutral 0 1 1 0 0 Very cost-effective 1 0 0 0 0 Cost-effective 0 0 0 0 0 Not cost-effective 0 0 0 0 0
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Design of selected studies
Key features of the design of the selected studies were summarized in Table 3. Six studies of ACEIs [33-38] were diverse in data sources, intervention and control groups. The 33 studies on ARBs showed much more consistency within each ARB drug class (losartan, irbesartan and valsartan) regarding control and data sources concerning the various clinical trials done in ARBs.
ACEIs
Six studies [33-38] evaluated the cost-effectiveness of ACEIs, all using a Markov model as the method for decision modeling. The transition probabilities in these Markov models, i.e. the sources and sizes of effectiveness data in these studies, were diverse. All six studies obtained their effectiveness data from more than one RCT [63-67] or from meta-analyses [37,38]. Only one of the studies [34] included a specific ACEI, enalapril, to compare with placebo, while the other five studies treated ACEIs as a group or drug class. ARBs were also included in the analytic model as a substitute for ACEIs when patients got cough side-effect in the two articles written by Adarkwah et al. [37,38].
ARBs
The 33 studies (included in 24 articles [32,39-61]) targeting ARBs have major similarities in study design. Fourteen evaluations for losartan [32,39-47] were based on The Reduction of Endpoints in Non-insulin Dependent Diabetes Mellitus with the Angiotensin II Antagonist Losartan (RENAAL) trial [62]. Eighteen evaluations of irbesartan [48-60] used data from the Irbesartan in Diabetic Nephropathy Trial (IDNT) [68] to assess the cost-effectiveness for patients with type 2 diabetes and overt nephropathy before 2004. Later the Irbesartan in Reduction of Microalbuminuria-2 (IRMA-2) [69] trial was added into the model to expand the progress of diabetic renal development from nephropathy back to the onset of MiA. The only study for valsartan was based on the MicroAlbuminuria Reduction With VALsartan (MARVAL) study [70].
All 14 losartan studies can be subdivided into two groups based on different time horizon. Eleven studies [32,39-42,46,47] were within-trial analyses, while the other three [43-45] extrapolated to beyond-trial time-horizon analyses. Ten within-trial analyses [32,39-42,47] used a straightforward method to calculate the effectiveness and cost. In this method, the patient-days spent in the stage of ESRD were estimated by subtracting the area under curve (AUC) of the Kaplan-Meier survival curve for time to the minimum of ESRD or all-cause death for both groups in the trial. The costs of ESRD were calculated by multiplying ESRD
days and daily cost of ESRD. Only one within-trial study [46] performed a Markov model as the analytic method to evaluate the cost-effectiveness. Three beyond-trial studies [43-45] used a Weibull model to prolong the time horizon to lifetime. Cumulative incidence of ESRD and life expectancy were assessed as the effectiveness measurements.
Irbesartan for overt nephropathy was compared with conventional treatment and amlodipine in five studies [48-51]. These five studies were based on the IDNT trial and a Markov model with five stages (from ‘ overt nephropathy ’ via ‘ double of serum creatinine ’ , ‘ ESRD + dialysis ’ and ‘ ESRD + transplant ’ to ‘ death ’ ) was developed to evaluate life expectancy and lifetime cost. In particular, Palmer et al. combined the IRMA-2 trial with the IDNT trial and applied a seven-stage Markov model, extrapolating the Markov model with a previous MiA state [48,49,51-60]. ‘ Early irbesartan ’ (standard antihypertensive therapy plus irbesartan at the onset of MiA) was then compared with conventional therapy and ‘ late irbesartan ’ or ‘ late amlodipine ’ (standard antihypertensive therapy plus administration of irbesartan/amlodipine once the patients reach the advanced diabetic nephropathy stage). Cost-effectiveness of Valsartan [61] was evaluated in one study using amlodipine as the control. A Markov model with seven stages was designed and QALYs were calculated as the effectiveness results.
34 Ta bl e 3 . S tu dy d es ig n o f e co no m ic e va lu at io ns o n A CE Is a nd A RB s St ud y, c ou nt ry /r eg ion So ur ce o f ef fe ct iv ene ss d at a In te rv en ti on g ro up Co nt rol g ro up De ci si on m od el T ype Ti m e h or iz on (y ea rs) Ev al ua ti on ty pe AC EI s Go la n e t a l. 19 99 US 32 UE RN N , L EA PP an d E AD N t ri al ‘T re at a ll’ s tr at eg y a (1 ) S cr ee n f or Mi A b; (2 ) S cr ee n fo r g ro ss pr ot ei nu ri a c. M ar ko v m od el w ith 5 st at es 10 CE A & C UA (L ife -y ea rs & QA LY s) Sa kt ho ng e t a l. 2 00 1 Th ai lan d 33 LE AN t ri al a nd the o pi nio n of ne ph ro lo gi st s En al ap ri l a t t he d os e of 1 0 m g/ da y Pl ace bo M ar ko v m od el w ith 4 st ag es 25 CE A (L ife y ears ) Ro se n e t a l. 20 05 US 34 UE RN N , E AD N , LE AN , H -M H st ud ie s a nd H OPE tr ia l M ed ic ar e f ir st -d ol la r co ve ra ge o f A CE Is Ye ar 2 00 5’ s M ed ic ar e pr ac tic e M ar ko v m od el a dd in g a c ar di ov as cu la r eve nt s c om po ne nt . Li fe ti me CE A & C UA (L ife -y ea rs & QA LY s) Ca m pb el l e t a l. 20 07 US 35 UE RN N , E AD N , H -M H s tu di es a nd IR M A-2 tr ia l AC EI t he ra py i n no rm oa lbu m in im ur ic , m ic ro al bu m in ur ic , an d m ac ro al bu m in ur ic pa tie nt s N o A CE I in iti at io n i n pa tie nt s M ark ov m od el 8 CE A (C VD e ve nt av oi de d, l ife s av ed , di al ys is pr eve nt ed , co m po si te en dp oi nt a vo ide d) Ad ar kw ah e t a l. 20 10 Ge rm any 36 EA DN a nd t w o m et a-an al ys es ‘T re at a ll’ s tr at eg y a (1 ) S cr ee n f or Mi A b; (2 ) S cr ee n f or Ma A c; (3 ) n o-sc re en in g an d n o-tr ea tm en t al te rn at iv e. M ar ko v m od el w ith 5 st at es 50 CUA (QAL Y) Ad ar kw ah e t a l, 2 01 1 N et her la nd s 37 EA DN a nd t w o m et a-an al ys es ‘T re at a ll’ s tr at eg y a (1 ) S cr ee n f or Mi A b; (2 ) S cr ee n f or Ma A c. M ar ko v m od el w ith 5 st at es 50 CUA (QAL Y)
Ta ble 3 . C on tin ue d St ud y, c ou nt ry /r eg ion So ur ce o f ef fe ct iv ene ss d at a In te rv en ti on g ro up Co nt rol g ro up De ci si on m od el T ype Ti m e h or iz on (y ea rs) Ev al ua ti on ty pe A R Bs Lo sar tan H er m an e t a l. 20 03 US 43 RE NA AL tr ia l Lo sar tan Pl ace bo d A r eg re ss io n-ba se d me th od 3. 5 / 4 CE A (N um be r o f E SR D da ys ) So uc he t e t a l. 20 03 Fr anc e 44 RE NA AL tr ia l Lo sa rt an ( in iti al d ai ly do si ng o f l os ar ta n w as 5 0 m g, w ith t he po ss ib ili ty o f t itr at io n to 1 00 m g/ da y) Pl ace bo d A r eg re ss io n-ba se d me th od 3. 5 / 4 CE A (N um be r o f E SR D da ys ) Bu rg es s e t a l. 20 04 Ca nad a 45 RE NA AL tr ia l Lo sar tan Pl ace bo d A r eg re ss io n-ba se d me th od 3. 5 / 4 CE A (N um be r o f E SR D da ys ) Sz uc s e t a l. 20 04 Sw it zer la nd 46 RE NA AL tr ia l Lo sa rt an ( in iti al d ai ly do si ng o f l os ar ta n w as 5 0 m g, w ith t he po ss ib ili ty o f t itr at io n to 1 00 m g/ da y) Pl ace bo d A r eg re ss io n-ba se d me th od 3. 5 / 4 CE A (N um be r o f E SR D da ys ) Se ng e t a l. 20 05 H on g K on g 47 (o nl y d at a o f H on g K on g we re inc lu de d) RE NA AL tr ia l Lo sar tan Pl ace bo d A r eg re ss io n-ba se d me th od 3. 5 CE A (N um be r o f E SR D da ys )
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36 Ta ble 3 . C on tin ue d St ud y, c ou nt ry /r eg ion So ur ce o f ef fe ct iv ene ss d at a In te rv en ti on g ro up Co nt rol g ro up De ci si on m od el T ype Ti m e h or iz on (y ea rs) Ev al ua ti on ty pe Ar re do ndo e t a l. 20 05 Me xi co 48 RE NA AL tr ia l Lo sar tan Pl ace bo d A v ar ia tio n o f t he cu m ul at iv e i nc id en ce co m pe ti ng r is k m et ho d / W ei bu ll m od el 25 ( lif e t im e) CE A (C um ul at iv e in ci de nc e o f E SR D, lif e e xpe ct an cy ) Vo ra e t a l. 20 05 UK 49 RE NA AL tr ia l Lo sa rt an ( 50 -1 00 m g QD) Co nve nt io na l an ti hy pe rt en si ve tr ea tm en t d (e xc lu di ng A CE Is or a ng io te ns in I I an ta go ni st s) W eib ul l m od el lif e t im e CE A (C um ul at iv e in ci de nc e o f E SR D, lif e e xpe ct an cy ) Ca ri de s e t a l. 20 06 US 50 RE NA AL tr ia l Lo sar tan Pl ace bo d A c um ul at iv e i nc id en ce co m pe ti ng r is k m et ho d / W ei bu ll m od el 25 ( lif e t im e) CE A (C um ul at iv e in ci de nc e o f E SR D, lif e e xpe ct an cy ) St af yl as e t a l. 20 07 Gr eec e 51 RE NA AL tr ia l Lo sa rt an ( 50 -1 00 m g QD) Pl ace bo d M ar ko v m od el w ith 6 st at es 3. 5 / 4 CE A (N um be r o f E SR D da ys ) de P or tu e t a l. 20 11 It al y, F ra nc e, G er m an y, Sw it zer la nd , US 52 RE NA AL tr ia l Lo sar tan St an dar d c ar e d St an da rd m et ho ds by c om pa ri ng t he ec on om ic o ut co me s de ri vi ng f ro m add itio na l l os ar ta n to s ta nd ar d c ar e v s st an dar d c ar e a lo ne 3.4 CE A (N um be r o f E SR D da ys ) Irbe sa rt an Ro db y R A e t a l. 20 03 US 53 ID N T t ri al Ir be sa rt an t itr at ed fr om 75 t o 3 00 m g/ day (1 ) ‘C on tr ol’ d; (2 ) A m lo di pi ne tit ra te d f ro m 2 .5 to 1 0 m g/ da y. M ar ko v m od el w ith 5 st ag es 25 CE A (L ife e xpe ct an cy )
Ta ble 3 . C on tin ue d St ud y, c ou nt ry /r eg ion So ur ce o f ef fe ct iv ene ss d at a In te rv en ti on g ro up Co nt rol g ro up De ci si on m od el T ype Ti m e h or iz on (y ea rs) Ev al ua ti on ty pe Pa lm er A J e t a l. 20 03 Be lg ium , F ra nc e 54 ID N T t ri al Ir be sa rt an t itr at ed fr om 75 t o 3 00 m g/ day (1 ) ‘C on tr ol’ d; (2 ) A m lo di pi ne tit ra te d f ro m 2 .5 to 1 0 m g/ da y. M ar ko v m od el w ith 5 st ag es 25 CE A (L ife e xpe ct an cy ) Co yl e D e t a l. 20 04 Ca nad a 55 ID N T t ri al Ir bess ar ta n (1 ) A m lo di pi ne ; (2 ) S ta nd ar d ca re d. M ar ko v m od el w ith 5 st ag es 25 CE A (L ife e xpe ct an cy ) Pa lm er A J e t a l. 20 04 UK 56 ID N T t ri al Ir be sa rt an 3 00 m g pe r d ay (1 ) ‘C on tr ol’ d; (2 ) A m lo di pi ne 10 m g p er d ay . M ar ko v m od el w ith 5 st ag es 25 CE A (L ife e xpe ct an cy ) Pa lm er A J e t a l. 20 04 US 57 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e (1 ) ‘C on tr ol’ d; (2 ) ‘ La te ir be sar tan ’ f M ar ko v m od el w ith 7 st ag es 25 CE A (Y ea rs f re e o f ES RD , c um ul at iv e in ci de nc e E SR D, lif e e xpe ct an cy ) Pa lm er A J e t a l. 20 05 Sp ai n 58 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e St and ar d an ti hy pe rt en si ve me dic at io ns d M ar ko v m od el w ith 7 st ag es 25 CE A (Y ea rs f re e o f ES RD , c um ul at iv e in ci de nc e E SR D, lif e e xpe ct an cy ) Pa lm er A J e t a l. 20 06 Sw it zer la nd 59 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e Co nve nt io na l an ti hy pe rt en si ve tr ea tm en t d in iti at ed w he n pa tie nt s h ad de ve lo pe d M iA . M ar ko v m od el w ith 7 st ag es 25 CE A (Y ea rs f re e o f ES RD , c um ul at iv e in ci de nc e o f E SR D, lif e e xpe ct an cy )
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38 Ta ble 3 . C on tin ue d St ud y, c ou nt ry /r eg ion So ur ce o f ef fe ct iv ene ss d at a In te rv en ti on g ro up Co nt rol g ro up De ci si on m od el T ype Ti m e h or iz on (y ea rs) Ev al ua ti on ty pe Pa lm er A J e t a l 20 06 Fr anc e 60 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e (1 ) ‘C on tr ol’ d; (2 ) ‘ La te ir be sar tan ’ f M ar ko v m od el w ith 7 st ag es 25 CE A & C UA (Y ea rs f re e of E SR D, l ife ex pe ct an cy , Q AL Y) Pa lm er A J e t a l 20 07 H un ga ry 61 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e ‘P la ce bo ’ d: st an dar d an ti hy pe rt en si ve me dic at io ns in iti at ed w he n p at ie nt s de ve lo pe d M iA . M ar ko v m od el w ith 7 st ag es 25 CE A (Y ea rs f re e o f ES RD , c um ul at iv e in ci de nc e E SR D, lif e e xpe ct an cy ) Pa lm er A J e t a l. 20 07 UK 62 IR M A-2 s tu dy a nd ID N T t ri al ‘E ar ly ir be sar tan ’ e (1 ) ‘C on tr ol’ d; (2 ) ‘ La te ir be sar tan ’ f M ar ko v m od el w ith 7 st ag es 25 CE A (Y ea rs f re e o f ES RD , c um ul at iv e In ci de nc e o f E SR D, lif e e xpe ct an cy ) Co yl e D e t a l. 20 07 Ca nad a 63 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e (1 ) ‘ La te ir be sar tan ’ f; (2) ‘Conve nt io na l’ d M ar ko v m od el w ith 7 st ag es 25 CE A (L ife e xpe ct an cy ) Ya ng W .C . e t a l. 20 07 Tai w an 64 IR M A-2 s tu dy a nd ID N T ‘E ar ly ir be sar tan ’ e (1 ) ‘ St and ar d’ d; (2 ) ‘ La te ir be sar tan ’ f; (3 ) ‘ La te am lo di pi ne ’ g M ar ko v m od el w ith 7 st ag es 25 CE A (L ife e xp ec ta nc y, nu m be r o f y ea rs fr ee o f E SR D, cu mu la ti ve in ci de nc e o f E SR D)
Ta ble 3 . C on tin ue d St ud y, c ou nt ry /r eg ion So ur ce o f ef fe ct iv ene ss d at a In te rv en ti on g ro up Co nt rol g ro up De ci si on m od el T ype Ti m e h or iz on (y ea rs) Ev al ua ti on ty pe An ne m an s e t a l. 20 08 Ch in a, T ai w an , M al ay sia , T ha ila nd , So ut h K or ea 65 IR M A-2 s tu dy a nd ID N T t ri al ‘E ar ly ir be sar tan ’ e (1 ) ‘ St and ar d’ d; (2 ) ‘ La te ir be sar tan ’ f; (3 ) ‘ La te am lo di pi ne ’ g M ar ko v m od el w ith 7 st ag es 25 CE A (C um ul at iv e in ci de nc e o f ES RD , n um be r o f da ys i n d ia ly si s, nu m be r o f y ea rs fr ee o f E SR D, l ife ex pe ct an cy ) Va ls ar tan Sm ith D G e t a l. 20 04 US 66 M AR VAL s tu dy Va ls ar tan Am lo di pi ne M ar ko v m od el w ith 7 st ag es 8 CUA (Qua lit y-ad ju st ed sur vi va l) a: n o s cr ee ni ng w as p er fo rm ed a t a ll a nd p at ie nt s s ta rt ed o n AC EI t he ra py a t t he t im e o f d ia gn os in g t yp e 2 d ia be te s b: p at ie nt s w er e s cr ee ne d f or M iA o nc e a y ea r a nd AC EI t re at m en t w as s ta rt ed i f t he t es t r es ul t i s p os it iv e c: p at ie nt s w er e s cr ee ne d f or M aA o nc e a y ea r a nd AC EI t re at m en t w as s ta rt ed i f t he t es t r es ul t i s p os it iv e d: s ta nd ar d a nt ih yp er te ns iv e t he ra py a lo ne , e xc lu di ng t he u se o f AC EI s, A RB s e: s ta nd ar d a nt ih yp er te ns iv e t he ra py p lu s a dm in is tr at io n o f i rb es ar ta n 3 00 m g/ d a t t he o ns et o f M iA f: s ta nd ar d a nt ih yp er te ns iv e t he ra py p lu s a dm in is tr at io n o f i rb es ar ta n 3 00 m g/ d o nc e t he p at ie nt s r ea ch t he a dv an ce d d ia be tic n ep hr op at hy s ta ge g: s ta nd ar d a nt ih yp er te ns iv e t he ra py p lu s a dm in is tr at io n o f a m lo di pi ne t it ra te d f ro m 5 t o 1 0 m g/ d o nc e t he p at ie nt s r ea ch t he a dv an ce d d ia be tic n ep hr op at hy s ta ge U ER N N = Us e o f e na la pr il t o a tt en ua te d ec lin e i n r en al f un ct io n i n n or m ot en si ve , n or m oa lb um in ur ic p at ie nt s w it h t yp e 2 d ia be te s m el lit us ; L EA PP = L on g-te rm st ab ili zi ng e ffe ct o f a ng io te ns in -c on ve rt in g e nz ym e i nh ib it io n o n p la sm a c re at in in e a nd o n p ro te in ur ia i n n or m ot en si ve t yp e I I d ia be tic p at ie nt s; EA D N = Th e e ffe ct of a ng io te ns in-con ve rt in g-en zy me in hi bi tion on d ia be tic ne ph rop at hy ; LE A N = Lo ng -t er m r en op ro te ct iv e e ffe ct o f a ng io te ns in -c on ve rt in g e nz ym e i nh ib it io n i n non-in su lin-de pe nd en t d ia be te s me lli tu s; H-M H = Ef fe ct s o f r am ip ri l o n c ar di ov as cu la r a nd m ic ro va sc ul ar o ut co m es i n p eo pl e w it h d ia be te s m el lit us : r es ul ts o f th e H OP E s tu dy a nd M IC RO -H OP E s ub st ud y; H OP E = Th e H ea rt O ut co m es P re ve nt io n E va lu at io n; R EN A A L = T he r ed uc tio n o f e nd po in ts i n n on -in su lin d ep en de nt di ab et es m el lit us w it h t he a ng io te ns in I I a nt ag on is t l os ar ta n; ID N T = Th e i rb es ar ta n i n d ia be tic n ep hr op at hy t ri al ; I M R A-2 = T he i rb es ar ta n i n r ed uc tio n o f m ic ro al bu m in ur ia -2 ; M AR VAL = T he m ic ro al bu m in ur ia r ed uc tio n w it h v al sa rt an . CE A = co st -e ffe ct iv en es s a na ly si s; C U A = co st -u ti lit y a na ly si s.
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Cost-effectiveness results
The key features and main results of all included evaluations are summarized in Table 4.
ACEIs
Of the six ACEIs’ studies, two [33,35] adopted a societal perspective. This contained additional cost analyses including productivity gains and losses, caregiver time costs. The other four [34,36-38] took the third party payer/health care perspective including only direct costs of nephropathy, ACEIs or other related treatment such as those for cardiovascular disease (CVD). All studies except one [33] favored ACEIs due to the cost-saving results. The exception was the evaluation from Golan et al. [33], showing that compared to ‘screen for MiA’ (patients were screened for MiA once a year and ACEI treatment was started if the test result is positive), the ‘treat all’ strategy with ACEIs (no screening was performed at all and patients started on ACEI therapy at the time of diagnosing type 2 diabetes) raised the costs by $300, but the results still supported ‘treat all’ strategy as very cost-effective. – It should be noted that these positive results were based on the comparison between ACEIs and no blood pressure (BP) control treatment but not other BP control interventions.
ARBs
Based on the RENAAL trial, all the results over 3.5 years indicated losartan was cost-saving or cost-neutral (Hong Kong) [32] comparing to placebo/conventional therapy. The cost savings per patients ranged from € 2,079 in Greece [46] to € 4,641 in France [47]. When the time horizon was prolonged to lifetime or 25 years, beyond-trial studies showed that the net cost savings by adding losartan to conventional therapy were € 9,182 in UK [44], € 1,861 in Mexico [43] and € 22,757 in U.S [45].
For irbesartan, results consistently showed cost-savings comparing with conventional therapy or amlodipine, even when already started at the onset of MiA. Such early start of irbesartan would economically be even more attractive as compared with late irbesartan starting at overt nephropathy. The five studies [48-51] based on the IDNT trial demonstrated that irbesartan for overt nephropathy could prolong life expectancy with 0.43 years (Canada) [50] to 0.74 years (U.S.) [48] and save € 7,075 (U.K.) [51] to € 19,132 (France) [49] per patient comparing with control over 25 years. When the MiA stage was introduced into the model, early irbesartan remained cost-saving at € 2,564 in Hungary [56] to € 57,871 in Canada [58] compared with control, being more cost-saving than late irbesartan.
The only study for valsartan [61] also supported the using of ARBs in patients with type 2 diabetes and MiA because of saving QALYs and costs. Over 8 years, valsartan treatment had 0.555 discounted QALYs advantage over amlodipine with savings at € 30,424 compared to amlodipine.
42 Ta bl e 4 . M ai n r es ul ts o f e co no m ic e va lu at io ns o n A CE Is a nd A RB s St udy , co un tr y/ re gi on D is cou nt r at e (p er a nn um) Pe rs pe ct iv e Co st c at eg or ie s D is co un te d l if e exp ect an cy / Q A LY In cr em en ta l c os t pe r p at ie nt s [y ea r o f v al ue ] In cr em en ta l c os t pe r p at ie nt (s ta nda rd iz ed to 20 11 E ur o) CE w it h In te rv en ti on s Ef fe ct s (%) Co st s (%) AC EI s Go la n e t a l. 19 99 US 32 3 3 So ci et al Th e c os t o f ES RD ( di al ys is & t ra ns pl an t) , AC EI s a nd sc re en in g 15 .6 3 y ea rs / 11 .8 2 QA LY s w ith ‘ tr ea t a ll’ , 15 .5 9 y ea rs / 11 .7 8 QA LY s w ith ‘ sc re en f or M iA ’, 1 5. 39 y ea rs / 11 .5 9 QA LY s w ith ‘ sc re en f or gr os s p ro te in ur ia’ ‘T re at a ll’ v s ‘sc re en f or M iA ’: $3 00 a ‘T re at a ll’ v s ‘sc re en f or M iA ’: €29 9 Ve ry c os t-e ffe ct iv e [‘T re at a ll’ vs . ‘s cr ee n fo r M iA ’: € 8, 06 2/ QA LY ] Sa kt ho ng et a l. 20 01 Th ai lan d 33 8 8 N ot me nt io ne d Th e c os t o f E SR D (h ae m od ia ly si s) an d AC EI 9. 04 y ea rs w ith en al ap ri l, 7 .5 4 y ea rs w ith c on tr ol -$ 1, 19 8 [1 99 9] -€ 1, 26 9 Co st sa vi ng [E na la pr il] Ro se n t a l. 20 05 US 34 3 3 M ed ic ar e a nd so ci et al (1 ) M ed ic ar e pe rs pe ct iv e: di re ct m ed ic al co st s a nd f ut ur e he al th c ar e cos ts . (2 ) S oc ie ta l pe rs pe ct iv e: add itio na l an al ys es inc lu de d pr od uc ti vi ty ga in s a nd l os se s, ca re gi ve r t im e cos ts 10 .5 5 y ea rs / 8. 36 Q AL Ys w ith M ed ic ar e f ir st -do lla r c ov er ag e o f A CE Is , 10 .3 0 y ea rs / 8. 13 Q AL Ys w ith a t t he t im e p ra ct ic e -$ 1, 60 6 [2 00 3] -€ 1,4 53 Co st sa vi ng [M ed ic ar e f ir st -do lla r c ov er ag e o f AC EI s]
