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      Impact on total population health and societal cost, and the implication on the actual cost-effectiveness of including tumour necrosis factor-α antagonists in management of ankylosing spondylitis: a dynamic population modelling study

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          Abstract

          Background

          Sequential treatment of ankylosing spondylitis (AS) that includes tumour necrosis factor-α antagonists (anti-TNF agents) has been applied in most of the Western countries. Existing cost-effectiveness (CE) models almost exclusively presented the incremental CE of anti-TNF agents using a closed cohort while budget impact studies are mainly lacking. Notwithstanding, information on impact on total population health and societal budget as well as on actual incremental CE for a given decision time span are important for decision makers. This study aimed at quantifying, for different decision time spans starting from January 1, 2014 in the Dutch society, (1) impact of sequential drug treatment strategies without and with inclusion of anti-TNF agents (Strategies 1 and 2, respectively) on total population health and societal cost, and (2) the actual incremental CE of Strategy 2 compared to Strategy 1.

          Methods

          Dynamic population modelling was used to capture total population health and cost, and the actual incremental CE. Distinguishing the prevalent AS population on January 1, 2014 and the incident AS cohorts in the subsequent 20 years, the model tracked individually an actual number of AS patients until death or end of the simulation time. During the simulation, data on patient characteristics, history of drug use, costs and health at discrete time points were generated. In Strategy 1, five nonsteroidal anti-inflammatory drugs (NSAIDs) were available but anti-TNF agents withdrawn. In Strategy 2, five NSAIDs and two anti-TNF agents continued to be available.

          Results

          The predicted size of the prevalent AS population in the Dutch society varied within the range of 67,145–69,957 with 44–46 % of the patients receiving anti-TNF agents over the period 2014–2034. The use of anti-TNF agents resulted in an increase in the annual drug costs (168.54–205.28 million Euros), but at the same time caused a decrease in the annual productivity costs (12.58–31.21 million Euros) and in annual costs of healthcare categories other than drugs (7.23–11.90 million Euros). Incremental cost (Euros) per QALY gained in Strategy 2 compared to Strategy 1 corresponding to decision time spans of 5, 10, 15 and 20 years improved slightly from 75,379 to 67,268, 63,938 and 61,129, respectively. At willingness-to-pay thresholds of 118,656, 112,067, 110,188 and 110,512 Euros, it was 99 % certain that Strategy 2 was cost-effective for decision time spans of 5, 10, 15 and 20, respectively.

          Conclusions

          Using the dynamic population approach, the present model can project real-time data to inform a healthcare system decision that affects all actual number of AS patients eligible for anti-TNF agents within different decision time spans. The predicted total population costs of different categories in the present study can help plan the organization of the healthcare resources based on the national budget for the disease.

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          Most cited references26

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          Decision Modelling for Health Economic Evaluation

          In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. [Ed.]
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            Prevalence of spondylarthropathies in HLA-B27 positive and negative blood donors.

            To determine the overall prevalence of spondylarthropathy (SpA) among whites. To screen for SpA symptoms, such as inflammatory back pain (IBP), joint swelling, psoriasis, and uveitis, or a specific family history, questionnaires were mailed to 348 blood donors (174 HLA-B27 positive and 174 HLA-B27 negative). From the responding 273 persons (78%; 140 B27 positive, 133 B27 negative), 126 were selected for further evaluation based on the symptoms reported. Of this group, 90 persons agreed to undergo physical examination (71.4%; 46 B27 positive, 44 B27 negative). There was no difference between the B27-positive and -negative groups in terms of age (mean +/- SD 38.4 +/- 10 versus 39.5 +/- 11 years) and sex ratio (67% versus 68% were men). In addition, 58 donors (32 B27 positive, 26 B27 negative) agreed to undergo magnetic resonance imaging (MRI) of the sacroiliac joints. A diagnosis of SpA and ankylosing spondylitis (AS) was made according to the European Spondylarthropathy Study Group criteria and the New York criteria. SpA was diagnosed in 20 persons: 19 of 140 B27-positive (13.6%) and 1 of 133 B27-negative (0.7%) subjects (15 male and 5 female). AS was diagnosed in 9 persons (7 male and 2 female; 45%), undifferentiated SpA (USpA) in 7 (5 male and 2 female; 35%), psoriatic arthritis (PsA) in 3 (2 male and 1 female; 15%), and chronic reactive arthritis (ReA; Reiter's syndrome) in 1 (male; 5%). On the basis of a B27 frequency of 9.3% among the population of Berlin (3.47 million persons), the estimated prevalence of SpA was 1.9%, AS was 0.86%, USpA was 0.67%, and PsA was 0.29%. The relative risk of developing SpA in B27-positive subjects was calculated as 20.7 (95% confidence interval 4.6-94.2; P = 0.001). Of 58 persons with IBP, sacroiliitis was detected by MRI in 15 of 32 B27-positive (46.9%) and 1 of 26 B27-negative (3.9%) subjects (P = 0.002). Four of these 16 donors did not fulfill diagnostic criteria for SpA. With a calculated prevalence of 1.9%, spondylarthropathies are among the most frequent rheumatic diseases in the white population. HLA-B27 positive persons carry a 20-fold increased risk of developing SpA. AS and USpA are the most frequent SpA subtypes. Persons with IBP who are B27 positive have a 50% likelihood of having sacroiliitis.
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              A practical guide for calculating indirect costs of disease.

              There may be some discussion about whether indirect costs should be taken into account at all in an economic appraisal, but there is certainly considerable debate about the proper way of estimating these costs. This reviews offers a practical guide for quantifying and valuing these indirect costs of disease, both at an aggregated level of general cost of illness studies, and in an economic appraisal of specific healthcare programmes. Two methods of calculating these costs are considered: the traditional human capital approach, and the more recently developed friction cost method. The former method estimates the potential value of lost production as a result of disease, whereas the latter method intends to derive more realistic estimates of indirect costs, taking into account the degree of scarcity of labour in the economy. All necessary steps in the estimation procedure and the data required at various points will be described and discussed in detail.
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                Author and article information

                Contributors
                +31 43 3877536 , an.tranduy@mumc.nl , an.tranduy@gmail.com
                a.boonen@mumc.nl
                m.vandeLaar@mst.nl
                severens@bmg.eur.nl
                Journal
                Cost Eff Resour Alloc
                Cost Eff Resour Alloc
                Cost Effectiveness and Resource Allocation : C/E
                BioMed Central (London )
                1478-7547
                7 October 2015
                7 October 2015
                2015
                : 13
                : 18
                Affiliations
                [ ]Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
                [ ]Division of Rheumatology, Department of Internal Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
                [ ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
                [ ]Caphri School for Public Health and Primary Care, Maastricht UMC+, Maastricht, The Netherlands
                [ ]Department of Rheumatology and Clinical Immunology, Twente University and Medisch Spectrum Twente, Enschede, The Netherlands
                [ ]Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
                Article
                44
                10.1186/s12962-015-0044-x
                4597433
                26451133
                ac53d4a9-0fdc-4688-9978-443dd7ec0e62
                © Tran-Duy et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 29 August 2014
                : 29 September 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Public health
                budget impact,health impact,cost-effectiveness,cost-utility,discrete event simulation,microsimulation,modelling,population dynamics,tumor necrosis factor,ankylosing spondylitis

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