br Fig nbsp xA Under focused XTEM bright

Fig. 4. Under-focused XTEM bright field micrographs of the He bubble formation in GIs of the He-implanted polycrystalline SiC sample with a fluence of 5 × 1015 cm–2 followed by annealing at 1000 °C for 30 min. (a) The inhibitor of apoptosis beam incident at an angle of 12° away from the c plane, (b) a high-resolution TEM image of the damaged layer with the electron beam close to [1210] zone axis and (c) the electron beam close to [0001].Figure optionsDownload full-size imageDownload high-quality image (1421 K)Download as PowerPoint slide

At the intermediate fluence of 1 × 1016 cm–2, the distribution of the bubbles is almost homogenous in the depth ranging from 720 to 860 nm below the surface, as shown in Fig. 5(a). Fig. 7 ;  Fig. 8 illustrate the distributions of diameters and number density of the bubbles observed in the sample implanted with a fluence of 1 × 1016 cm–2. The diameters of the bubbles range from 0.9 to 6.7 nm and a mean diameter is approximately 3.0 nm. The mean number density of the bubbles is estimated to be 7.6 × 1022 m–3 and the maximal density is located at a depth of approximately 810 nm, where corresponds to the maximum peak concentration of He atoms. Some bubbles in the upper damaged layer appeared as pearl-like distribution. A weak beam dark field image presents numerous dislocation loops in these regions, as indicate by white arrows in Fig. 5(b). Because the bubbles are preferentially nucleated and grown on dislocation loops, pearl-like distributions of the bubbles were found.

Fig. 5. XTEM micrographs of the He bubble formation in GIs of the He-implanted polycrystalline SiC sample with a fluence of 1 × 1016 cm–2 followed by annealing at 1000 °C for 30 min, (a) with a bright field in under-focused condition, (b) with a weak beam dark field (using g/5 g, g = 01–1–1).Figure optionsDownload full-size imageDownload high-quality image (580 K)Download as PowerPoint slide

Fig. 6. Under-focused XTEM bright field micrographs of the He bubble formation in GIs of the He-implanted polycrystalline SiC samples with fluences of (a) 2.5 × 1016, (b) 5 × 1016 and (c) 1 × 1017 cm–2 followed by annealing at 1000 °C for 30 min (d) XTEM weak beam dark filed image of the damaged layer in the He-implanted polycrystalline SiC sample with a fluence of 2.5 × 1016 cm–2 followed by annealing at 1000 °C for 30 min (using g/5 g, g = −1010).Figure optionsDownload full-size imageDownload high-quality image (1832 K)Download as PowerPoint slide

At the high fluences of 2.5 × 1016–1 × 1017 cm–2, the distribution of the bubbles is similar, as shown in Fig. 6(a), (b) and (c). For comparison the intermediate fluence of 1 × 1016 cm–2, the width of the bubble layer was considerably broadened. Some tiny bubbles grew in the highly disordered region toward the surface because He atoms can diffuse into this region upon annealing [21]. On the basis of the distribution of the bubble density with depth, the layer of the bubbles can be divided into two different regions, named A and B. In the upper layer of the bubbles, Region A, is weakly affected by the implantation process. A low density of tiny dislocation loops and stacking faults in this region was found for the sample implanted with a fluence of 2.5 × 1016 cm–2, as shown in Fig. 6(d). Under-focused conditions showed a low number density of the bubbles distributed along chains. In the deeper layer of the bubbles, Region B, contains a large number density of the bubbles. In the middle of region B, is located at a depth of 810 nm below the surface. The microstructural image shows large bubbles with a low number density as compared to those of its periphery. This phenomenon is attributed primarily to Ostwald mechanism [22]. The distributions of the number density and diameter of the bubbles were analyzed, as provided in Fig. 7 ;  Fig. 8. For the sample implanted with a fluence of 2.5 × 1016 cm–2, the distribution of the bubbles ranges from approximately 640 to 860 nm below the surface as illustrated in Fig. 7(b). The maximal density is located at approximately 800 nm and the mean density is estimated to be approximately 8 × 1022 m–3. Fig. 8(b) illustrates that the diameters of the bubbles range from 0.7 to 5.7 nm, and the mean diameter is approximately 2.1 nm. For the sample implanted with a fluence of 5 × 1016 cm–2, the distribution of the bubbles at depths ranges from about 610 to 860 nm below the surface, as illustrated in Fig. 7(c). The maximal density is located at a depth of approximately 780 nm and the mean density is estimated to be approximately 1.3 × 1023 m–3. Fig. 8(c) illustrates that the diameters of the bubbles range from 1.3 to 13.2 nm, and the mean diameter is approximately 3.4 nm. For the sample implanted with a fluence of 1 × 1017 cm–2, the distribution of the bubbles at depths ranges from about 580 to 860 nm below the surface, as illustrated in Fig. 7(d). The maximal density is located at a depth of approximately 780 nm and the mean density is estimated to be approximately 1.5 × 1023 m–3. Fig. 8(d) illustrates that the diameters of the bubbles range from 1.5 to 21.6 nm, and the mean diameter is approximately 4.1 nm.

CX4945 br Effects and Costs br Utility scores were combined

Effects and Costs

Utility scores were combined with life expectancy to calculate quality-adjusted life-years (QALYs). Utility scores provide a single index value for health-related quality of life, ranging from 0 (death) to 1 (optimal health), and were estimated on the basis of EuroQol five-dimensional questionnaire [16]. The disutilities for the health states, local/regional recurrence, and metastases were assumed to be independent of treatment site and disease stage.

Costs were estimated as the costs per health care activity in the studied organizations in 2013 multiplied by the required health care activities on the basis of opinions of clinical experts (Appendix A). Costs that potentially differed between the two logistical processes were mainly focused on because only these costs could influence the incremental cost-effectiveness analysis. Three dimensions of costs were included in the Markov model:1.Event costs: Costs for all the expected health care activities in need incurred when the health state of a patient changed (e.g., a recurrence).2.Follow-up (health state) costs: All follow-up costs for the first 5 years after treatment until the next event. From the sixth year onward, follow-up costs were assumed to be zero.3.Intervention costs: The intervention cost were partly covered by the costs associated with the implementation of the redesign (NWF), which is based on the purchase cost for the endoscope (Pentax chip-on tip) with unit (estimated at €50,000), and spread over 1000 patients to be diagnosed (1250–1400 patients expected in 5 years). The intervention costs also covered the costs for unnecessary activities, which included the costs for patients whose biopsy under local anesthesia failed and an additional biopsy under full anesthesia had to be performed. They also included the costs for patients who received a diagnostic PET-CT in CX4945 therapy position with mask, but in the end appeared to have no indication for radiotherapy. Only the costs for the health care activities were included in this study.

To account for inflation rates, future QALYs and costs were discounted at 1.5% and 4%, respectively [17].

Markov Model Analysis

The life-years, QALYs, and expected total costs were calculated per tumor site and stage (eight patient groups) for the RWF as well as the NWF. The incremental cost-effectiveness ratio (ICER) was calculated by dividing the incremental (NWF minus RWF) costs by the incremental QALYs. This ICER represented the additional costs of one QALY when the NWF was implemented for the specific tumor site. A treatment is considered cost-effective when the ICER is below the price a patient (or the society) is willing to pay for an additional QALY (ceiling ratio). A ceiling ratio of €80,000 was adopted because saprophytes is the informal ceiling ratio for high-burden diseases in The Netherlands [18]. This means that society is willing to pay €80,000 for an additional year in perfect health. This ceiling ratio is relatively high compared with those used in other countries, for example, £20,000 to £50,000 in the United Kingdom [19]. Therefore, a more conservative ceiling ratio of €20,000 (used for nondestructive diseases in The Netherlands) was also considered [18].

Sensitivity Analysis

Probabilistic sensitivity analysis using Monte-Carlo simulation was performed to account for the uncertainty of the input parameters in the model [3]. The simulation incorporated 5000 iterations. The results of these simulations were illustrated using cost-effectiveness acceptability curves.

Extrapolation of Results

Patient outcomes were extrapolated to organizational and national levels (The Netherlands). The incremental net monetary benefit (INMB) was multiplied by the number of patients treated. The INMB is a representation of incremental gains/benefits (QALYs multiplied by the ceiling ratio) minus incremental costs [20]. For example, an INMB of €100 means that the total benefit of the proposed change exceeds the total costs with €100 on the long run. The INMB on the organizational level presented the number of patients treated within the studied organization (based on the annual reports of the two participating organizations) multiplied by the INMB. This organizational INMB represented the incremental benefit from the perspective of the health care payers and did not represent the incremental benefit or cost for the specific organization. The INMB extrapolated to the national level for The Netherlands was calculated by multiplying the incidence numbers from the Integral Cancer Centre for the four included treatment sites [21] and the INMB.

br Source of financial support The writing of this

Source of financial support: The writing of this article was supported in part by Abbott. However, the authors summarized their independent professional opinion and take full responsibility for potential errors in the article.

clinical trials; online communities; patient-reported outcomes; social media

Introduction

Patient-reported outcomes (PROs) are increasingly recognized as important tools in adding value to the drug review and evaluation process

because they provide unique perspectives on medical conditions or their therapies that are known only to the patient

. “Content validity” describes the extent to which a PRO intended to assess such subjective outcomes actually measures the concept of interest

and is an ongoing process that relies on the generation of qualitative and quantitative evidence. Crucially, PRO measures must reflect patient concerns relative to the concept being assessed and, therefore, documentation of content validity relies on patient input from the target tropisetron of patients

. Qualitative studies are used to generate appropriate items and domains; to ensure the instrument is comprehensive relative to its intended measurement concept, population, and context of use; and to ensure patient understanding of the instrument, that is, instructions, items, and response options through cognitive debriefing

. Best practices usually include either individual interviews or focus groups with participants who are experiencing the target condition or have recent experience with it. These traditional methods of collecting qualitative data to support the content validity of a new or existing PRO instrument, however, are labor intensive, time consuming, and relatively expensive. Although detailed figures are not available for specific parts of the instrument, development estimates from the New England Research Institutes suggest that developing a PRO from beginning to end takes at least 24 months and costs between

decision making; dynamic simulation modeling; health care delivery; methods

Background to the Task ForceIn October 2013, the ISPOR Health Science Policy Council recommended to the ISPOR Board of Directors that an ISPOR Emerging Good Practices for Outcomes Research Task Force be established to focus on dynamic simulation modeling methods that can be applied in health care delivery research and recommendations on how these simulation techniques can assist health care decision makers to evaluate interventions to improve the effectiveness and efficiency of health care delivery. The Board of Directors approved the ISPOR Simulation Modeling Emerging Good Practices Task Force in November 2013.The task force leadership group is composed of experts in modeling, epidemiology, research, systems and industrial engineering, economics, and health technology assessment. Task force members were selected to represent a diverse range of perspectives. They work in hospital health systems, research organizations, academia, and the pharmaceutical industry. In addition, the task force had international representation with members from Canada, The Netherlands, Colombia, and the United States.The task force met approximately every five weeks by teleconference to develop an outline and discuss issues to be included in the report. In addition, task force members met in person at ISPOR International meetings and European congresses. All task force members reviewed many drafts of the report and provided frequent feedback in both oral and written comments.Preliminary findings and recommendations were presented in forum and workshop presentations at the 2014 ISPOR Annual International Meeting in Montreal and ISPOR Annual European Congress in Amsterdam. In addition, written feedback was received from the first and final draft reports’ circulation to the 190-member ISPOR Modeling Review Group.Comments were discussed by the task force on a series of teleconferences and during a 1.5-day task force face-to-face consensus meeting. All comments were considered, and most were substantive and constructive. Comments were addressed as appropriate in subsequent versions of the report. All written comments are published at the ISPOR Web site on the task force’s Webpage: http://www.ispor.org/TaskForces/Simulation-ModelingApps-HCDelivery.asp. The task force report and Webpage may also be accessed from the ISPOR homepage (www.ispor.org) via the purple Research Tools menu, ISPOR Good Practices for Outcomes Research, heading: Modeling MethodsIn the course of task force deliberations, in response to specific comments and suggestions from reviewers, and a growing concern about length, sclerenchyma became apparent that two task force reports would be needed to be thorough, covering the essential points, yet keep the report readable and digestible. With Value in Health’s permission, the material has been split into two articles.This first article is a primer on how dynamic simulation modeling methods can be applied to health system problems. It provides the fundamentals and definitions, and discusses why dynamic simulation modeling methods are different from typical models used in economic evaluation and relevant to health care delivery research. It includes a basic description of each method (system dynamics, discrete event simulation, agent-based modeling), and provides guidance on how to ascertain whether these simulation methods are appropriate for a specific problem via the SIMULATE checklist developed by the task force.The second report will provide more depth, delving into the technical specifications related to the three dynamic simulation modeling methods. It will systematically compare each method across a number of features and provide a guide for good research practices for the conduct of dynamic simulation modeling. This report will appear in the March/April 2015 issue of Value in Health.

br These case studies have demonstrated the

These case studies have demonstrated the feasibility of applying the checklist without requiring any substantial reanalysis of the original assessments. The information required to assess whether other sources of uncertainty will resolve over time (point 5 on the checklist), however, requires information that is not commonly reported during an appraisal process. It is also recognized that some amendments might be required when cost-effectiveness is not the prime consideration. Furthermore, the application of the checklist alone is unlikely to be sufficient because no quantitative analysis can capture all aspects of scientific and social value judgments. Therefore, the most relevant question is whether these methods offer a practical and useful starting point for deliberation and add to the transparency and accountability of adoption decisions.

AcknowledgmentsWe thank the members of the Advisory Group for this aurora kinase inhibitor project: Kalipso Chalkidou; Iain Chalmers; Sarah Garner; Chris Henshall; John Hutton; Catherine Law; Jonathan Michaels; Bhash Naidoo; Jon Nicholl; Hans Severens; Ken Stein; Sean Tunis; and Tom Walley, as well as Steve Holland and Tony Hope, as ethics advisors to the project. Any errors or omissions are the sole responsibility of the authors. The views and opinions expressed are also those of the authors and do not necessarily reflect those of the Advisory Group, the Health Technology Assessment program, the National Institute for Health Research, the National Institute for Health and Care Excellence, the National Health Service, or the Department of Health.Source of financial support: This article is based on research funded by the Medical Research Council and the National Institute for Health Research Health Technology Assessment (NIHR-HTA) program (project no. 06/90/99) and is published in full in the Health Technology Assessment series. Visit the HTA program Web site for further project information.

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1.10 billion gain compared with chemotherapy alone, and adding pertuzumab is associated with a

case-mix; costs adjustment; health care costs; HSM Index

Introduction

The burden of chronic diseases continues to grow in Western countries, where Codon accounts for almost 80% of total health care costs. Therefore, several health care systems have focused on controlling the growth in spending on health services while ensuring access to appropriate care

. General practice represents the most relevant setting for a cost assessment because general practitioners (GPs) constitute the primary access to health services, and control the diagnosis, management, prescribing, and referral of patients with chronic diseases. Previous studies conducted in the primary care setting in the United States reported mean yearly costs of

cardiovascular disease; healthy adherer effect; medication adherence; statins

Introduction

Several large randomized controlled trials (RCTs) and meta-analyses have provided convincing evidence for the benefits of statins in the primary and secondary prevention of cardiovascular (CV) events [1] ;  [2]. A recent meta-analysis including 18 RCTs and almost 57,000 high-risk primary prevention patients have demonstrated that statins can reduce the risk of cardiac events by 27% and all-cause mortality by 14% during a median 5 years of follow-up [3].

In RCTs, the adherence to study medication has generally been good. In real life, however, many patients adhere poorly to preventive medications, such as statins, and the benefits observed in highly adherent RCT populations may not be substantiated. A meta-analysis of 44 epidemiological studies estimated that the prevalence of poor adherence to statins (defined as consuming <80% of the prescribed medication) is as high as 46%, which would translate to 47 excess CV deaths per 100,000 Americans offered statin therapy per year [4]. Only one of the studies, however, included in that meta-analysis investigated the risk of CV events in relation to statin adherence in primary prevention [5]. This observational study found that good adherers had a 20% lower risk of CV events than poor adherers. In fact, some observational studies of primary prevention populations have reported much larger reductions in the risk of CV events (up to ~40%) and all-cause mortality (up to 45%) for high versus low levels of statin adherence [5]; [6]; [7]; [8]; [9]; [10]; [11] ;  [12].

br Fig nbsp xA XRD patterns of a

Fig. 3. XRD patterns of (a) Gd2O3 and (b) CeO2 films deposited at 300 K and oxygen partial pressure of 2 Pa.Figure optionsDownload full-size imageDownload high-quality image (221 K)Download as PowerPoint slide

Fig. 4. XRD patterns of (a) Gd2O3 and (b) CeO2 films deposited at 873 K and 2 Pa oxygen partial pressure.Figure optionsDownload full-size imageDownload high-quality image (319 K)Download as PowerPoint slide

Fig. 5 shows the XRD patterns of CeO2/Gd2O3 multilayers deposited at the substrate temperature of 300 K and oxygen partial pressure of 2 Pa. The XRD pattern of the multilayer films deposited at 300 K shows FCC polycrystalline structure for ceria with reflections from (111), (200), (220) and (311) planes. Similar results have been obtained by Balakrishnan et. Al [43]. In contrast, the XRD pattern does not contain any unique peaks from the Gd2O3, except for the mild skewing of the (111) peak of CeO2 at about 32° (2θ). The crystallite forms amorphous structure at room temperature (Fig. 3). The intensity of the (111) reflection increases with increase in the CeO2 layer thickness, which Cyclo due to the increase in the CeO2 content with increase in the layer thicknesses.

Fig. 5. XRD pattern of CeO2/Gd2O3 multilayers with different CeO2 layer thickness deposited on Si (100) substrates at substrate temperature of 300 K and oxygen partial pressure of 2 Pa.Figure optionsDownload full-size imageDownload high-quality image (311 K)Download as PowerPoint slide

Fig. 6 shows the CeO2/Gd2O3 multilayer films deposited at the substrate temperature of 873 K and oxygen pressure of 2 Pa. The XRD pattern shows the formation of polycrystalline films with increased number of peaks and with higher crystallinity at higher substrate temperature. The XRD pattern contains peaks from (111), (200), (220), (311), (222), (400), (331), (420) reflections of c-CeO2 and (211), (222), (123), (400), (440), (622), (444), (800), (662), (840) reflection of c-Gd2O3. The non-overlapping unique peaks such as (211) and (123) reflections were used to identify Gd2O3. It must be pointed out diploid the strong skewing seen in between (111) and (200) reflections of the multilayer deposited at room temperature tends to form (123) reflection of Gd2O3 at 673 K. The FWHM values decrease as the CeO2 layer thickness increases. The multilayer film deposited at 873 K shows a clear polycrystalline structure than that of the films grown at 300 K. The crystallite size was calculated by using equation 1 and listed in Table 4. The crystallite size of CeO2 calculated for the (111) peak of multilayer film grown at 300 K is found to vary from 7 to 11 nm with the increase in the CeO2 layer thickness from 5 to 30 nm. The crystallite size calculated for the (200) peak of CeO2 in the multilayers deposited at 873 K is found to vary from 11 to 24 nm with the increase in the CeO2 layer thickness from 10 to 30 nm. Unlike that Cyclo of the crystallite size of CeO2, the crystallite size of Gd2O3 was calculated for the films deposited at 873 K using (123) reflection and are found to be in the range of 12–15 nm. This is due to the constant layer thickness of Gd2O3. The decrease in FWHM values of the peaks for the multilayers deposited at 873 K confirms better crystallinity of phases of the multilayer film.

br Fig nbsp xA a Crystallized volume

Fig. 6. (a) Crystallized volume fraction α plotted as a function of temperature at different heating rates for Exo.1which is shown in Fig. 4, (b) plots of ln[−ln(1−α)] versus ln(t).Figure optionsDownload full-size imageDownload high-quality image (312 K)Download as PowerPoint slide

Fig. 7. (a) Crystallized volume fraction α plotted as a function of temperature at different heating rates for Exo.2 which is shown in Fig. 4, (b) plots of ln[−ln(1−α)] versus lnt.Figure optionsDownload full-size imageDownload high-quality image (308 K)Download as PowerPoint slide

In general, the JMA equation is related to kinetics of phase Glycerol involving nucleation and growth that can be computed under both isothermal and non-isothermal conditions [29]; [30] ;  [31]. The opposite of isothermal, non-isothermal experiment can be better performed over time and temperature.

During DSC, because of the heating rate is controlled, so the temperature can be expressed as:equation(3)T=T0+βtT=T0+βtt: Current time.

According to the latest thermodynamic theory, the Avrami exponent n is determined by the equation [19]:equation(4)n(t)=d ln[−ln(1−α)] dlntn: Avrami exponent related to the operating crystallization mechanism with detailed information Glycerol on nucleation and growth behavior. The computation of n is d/m for site saturation and d/m + 1 for continuous nucleation, with m as growth mode parameter where m with value of 1 is for interface-controlled growth, and 2 is for volume diffusion-controlled growth.d: Growth dimensionality (for example d = 3, 2 and 1 for three, two and one dimensional growth) respectively [32]. The computation of n from the slopes of linear plots ln[−ln(1−α)] versus lnt at different temperatures are given in Fig. 6 ;  Fig. 7b.

The nucleation and growth mechanisms are convertible during the whole crystallization procedure of an amorphous alloy, and different nucleation and growth behaviors exhibit at different stages. Thus, n can not be a constant as the crystallization proceeds.

With the condition of forming nuclei during heating at known rate it is dominant, n is equal to (d/m + 1); and condition that nuclei formed in any previous heat treatment prior to thermal analysis, n is equal to d/m [32]. Taking Ni53Nb20Ti10Zr8Co6Ta3 powders milled for 40 h into consideration, n is found to be (d/m + 1) because nuclei are originated form during the heating process. According to Fig. 6b for the first step, the Avrami exponent n for temperature ranges from 3.06 to 4.29, displaying that the growth mechanism is typical interface-controlled three-dimensional growth. In contrast, for the second step (see Fig. 7b) the values of n fluctuate from 1.50 to 2.93 and the average value is equal to 2.05 (≈2.0). This manifests that the crystallization process simultaneously occurs with three, two and one dimensional growth, and the major growth mechanism is volume diffusion-controlled two-dimensional growth.

3.3. Characterization of as-sintered alloy

Fig. 8 shows XRD pattern of bulk Ni53Nb20Ti10Zr8Co6Ta3 prepared by SPS at 1273 K for 5 min from the 40 h milled powders. Comparison with Fig. 1f, the crystallization of amorphous phase arises during the SPS. Typical amorphous structure is not clearly revealed in the diffraction pattern. The reactions of constituent elements produce different intermetallic compounds. The peak of constituent elementary phase is not detected. From the diffraction pattern, the as-sintered alloy is composed of Ni3Ti0.6Zr0.33, Nb0.1Ni0.9, Ni3Ti, NbNi3, Co4TiZr, NbNi and Ni2.67Ti1.33 phases. Fig. 9a shows the SEM micrograph of the sintered alloy after polishing and then etching with a solution of aqua regia. Based on the difference in imaging contrast and microstructural morphology, the microstructure can be intuitively classified into three types of zones, as marked by A, B and C. Zones A and B seem to distribute tendencially in C matrix. Zone B is in particle shape and on the scale of nanometer to submicron. According to the EDS analysis results listed in Table 3, zones A, B and C possibly consist of Co4TiZr + NbNi + Ni2.67Ti1.33, Ni3Ti + NbNi3 and Ni3Ti0.6Zr0.33 + Nb0.1Ni0.9, respectively. Different intermetallic compounds concomitantly form in every type of zone, although they are difficult to discern clearly in the SEM image. So, the size of individual phase is really quite fine in the as-sintered alloy, as shown in Fig. 9b (TEM image) and Fig. 9c (SEM fractograph). The microstructure is a mix of nano-scale grains and submicron grains.

br As a result of both

As a result of both government policies and pharmaceutical industry strategies, medicines prices differ among countries [8] ;  [9]. Differences may occur at different price levels; for instance, a country might have a comparably low ex-factory price level (i.e., the manufacturer price), but might have consistently high “end prices” (pharmacy retail prices) for the patients due to the adding of distribution remuneration such as margins and/or value-added tax rate [10] ;  [11].

One of the aims of both European and New Zealand medicines policies is to ensure that AZD-9291 the public has access to affordable medicines [12]. New Zealand has been successful in containing pharmaceutical costs, primarily via the policies of the Pharmaceutical Management Agency of New Zealand who manages most of the pharmaceutical expenditures [13] ;  [14].

In New Zealand, medicines are classified as either funded medicines or nonfunded medicines. Funded medicines are listed on the Pharmaceutical Schedule and are subsidized by the government from the pharmaceutical budget

. In New Zealand, medicines will cost the patient either NZ

patient-reported outcome; performance measurement; PRO-PM; quality

Background

A foundation for continuous quality improvement is to measure and compare care across practices and providers to translate successful management strategies to others [1]. Performance measurement has traditionally relied on routinely collected clinical information such as rates of hospital readmission, infections, procedural complications, survival, or laboratory values. But the ultimate impact on outcomes experienced by patients, such as symptoms, functional status, and health-related quality of life, have rarely been assessed.

Collection and analysis of patient-reported outcome (PRO) measures is increasingly considered a standard approach for evaluating these experiences [2]; [3]; [4] ;  [5]. A PRO is defined as information about the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else [3]. A patient-reported outcome measure (PROM) is a questionnaire used to elicit information directly from respondents. Inclusion of patients’ direct reports about how they feel and function in quality assessment programs through the use of patient-reported outcome-based performance measures (PRO-PMs), and particularly in accountability and value-based payment initiatives, would increase the patient-centeredness of these activities [6]; [7] ;  [8]. PRO measurement is already common in clinical trials and is of rising interest in comparative effectiveness research, routine clinical practice, and electronic medical record systems [9]; [10]; [11]; [12]; [13] ;  [14].

Beyond patient-centeredness, there are additional rationales to include PROs in performance measurement. Recent data suggest that patients’ self-reported symptoms and health status are associated with the use of medical services (e.g., emergency room visits and hospitalizations), costs, outpatient medication compliance, and survival [15]; [16]; [17] ;  [18]. The process of patient self-reporting itself can improve symptom management, quality of life, communication, and satisfaction with care [19]; [20]; [21] ;  [22]. Moreover, symptoms and functional status impairment are far more common than serious complications of treatment, such as hospitalizations or death [23]. As the ultimate end users of services, patients selecting a treatment or provider may have interest in outcomes based on previous reports of patients like themselves.

There is currently limited understanding in the PRO methodology community about performance measurement procedures, and a similarly limited understanding in the performance measurement community about methodological challenges involved with developing, administering, and analyzing PRO data. Therefore, there is a need for a practical blueprint to bring these two fields together and describe methodological best practices for developing, testing, implementing, and interpreting PRO performance measures that can be used as criteria by measure developers and credentialing organizations to evaluate candidate measures.

br The model summaries provided in Table xA

The model summaries provided in Table 2 ;  Table 3 show a clear pattern over time, with the ScHARR model commissioned by NICE [23]; [25] ;  [26] clearly a pivotal point in model evolution. There is GDC-0994 in modeling technique, model structure, input data, and assumptions across the six models developed before the ScHARR model [14]; [15]; [16]; [17]; [18]; [19]; [20]; [21] ;  [22]. In contrast, models developed after the ScHARR model converge with respect to the technique and basic structure. In line with other recent reviews [7] ;  [9], one feature common to all included models was the use of EDSS to model disability progression, in spite of criticism of this instrument for its inability to capture relevant clinical milestones [34]. As one would expect in a review of models taking a UK perspective, the impact of changes in guidance issued by NICE over time is also apparent, both in terms of the recommended discount rates for costs and utilities and also in terms of the balance between the risks of extrapolation of limited clinical effectiveness data versus the desire for a lifetime horizon. Time horizons varied, with 5, 8, 10, 20 years, and lifetime (represented as 50 years in two models) all used; more recent models have used longer time horizons. Short-term studies would have a tendency to underestimate the cost-effectiveness of DMTs because the m###http://www.amino-11-ddutp.com/image/1-s2.0-S2093791110120010-gr2.jpg####ain advantage of using DMTs is to postpone the development of severe MS, which occurs after a longer time period.

Of the models published before the ScHARR model in 2003 [23], two appeared to be decision tree models (though were not explicit in stating their model type) [14]; [15]; [16] ;  [18], one described itself as semi-Markov [21], one simulated individual patients [19] ;  [20], whereas two used forms of regression analysis [17] ;  [22]. The decision tree and Markov-type models [14]; [15]; [16]; [18] ;  [21] used various cohort structures based on different subsets of EDSS scores. The regression models attempted to consider the area under the EDSS score–time curve, in spite of the EDSS score being a series of ordered categories rather than a cardinal number amenable to such analysis [17] ;  [22]. Only one of the early models attempted to incorporate progression from RRMS to SPMS into its structure in any form [21]. This model applied inputs from the SPMS population to all patients with an EDSS score of 4.5 or above [21], which is not consistent with disease progression in clinical practice [35]. This same model was also the only early example to incorporate death and treatment withdrawal [21]. None of the early models reported incorporating the disutility or costs of adverse events (AEs), subgroup analyses, or probabilistic sensitivity analysis. All early studies provided results from an NHS and PSS perspective, though it was not the primary perspective for all the studies—MS has considerable effects on productivity for both patients and carers; therefore, some models provided results from a societal perspective [17]; [18] ;  [21]. NICE, however, specifies an NHS and PSS perspective as the basis for decision making, and UK models tend therefore to follow this approach; greater variation in perspective would be expected had other countries been included in this review. Costs were all discounted by the then-standard 6%, but utilities were not consistently discounted by the then-standard 1.5%.

As discussed in the Introduction, the controversy surrounding the first NICE appraisal of beta interferons and glatiramer acetate led to the commissioning of a new economic model by NICE. The commissioned model was produced by a consortium led by ScHARR and is available on the NICE website [25] along with a short addendum addressing new utility data that became available after the main report had been submitted but before the appraisal process was completed [26]. The effect of this process on those modeling DMTs in MS from a UK perspective is apparent from the results presented in Table 2 ;  Table 3, and after the ScHARR model all models describe themselves explicitly as being based on the ScHARR model. This new model defined a Markov structure based on the full set of EDSS scores (including the half-number scores as well as the whole-number scores) incorporating RRMS EDSS states 0–10, SPMS EDSS states 2–10, and a general death state. Mortality was modeled either as disease progression to EDSS state 10 (MS death), or as a result of other causes at any model stage (general death). Patients could transition from RRMS to SPMS at any point, and adverse effects and withdrawal from treatment were explicitly considered. Extrapolation of the short-term clinical efficacy data available at the time was balanced with the long-term nature of the disease through the choice of a 20-year time horizon. Comprehensive scenario analyses and probabilistic sensitivity analyses were also reported, addressing a criticism of many of the previously submitted models that had presented limited uncertainty analyses. Subsequent models have clearly converged to a standardized structure based on that used in the ScHARR model, but with two modifications. First, the number of states has been reduced from the full EDSS set used in the ScHARR model down to a set based on whole-number states, with each half-number state grouped with the one above. The exception to this is the EDSS 9.5 state, which is grouped in with EDSS state 9 (and 8.5). Second, the separate MS death (EDSS state 10 in each of RRMS and SPMS) and general death states have been combined into a single absorbing state. In all the models developed after the ScHARR model, costs and utilities were both discounted by the current UK standard of 3.5%. In spite of the adoption of this standardized structure, variation has been apparent in the model assumptions, as will be discussed below, and also with respect to some aspects of the data in which there are problems with lack of consensus and openness of data, with consequences for replicability.

br Following the bivariate analysis described above separate models were

Following the bivariate analysis described above, separate models were built for benefit ratings and risk ratings. Benefit ratings were regressed on the BFI personality traits identified from the bivariate analysis along with the GRA categories, sex, and therapeutic area. A forward and backwards stepwise regression selection method was used to determine the final model with the best model fit [37]. Variables with nonstatistically significant estimates (>0.05) were removed at each iteration. The evaluation of the benefit ratings and the PRA categories, sex, and therapeutic area followed the same model-building approach as above. This process was replicated for building the models for the risk ratings. All parameter estimates with statistically significant results at the less than 0.05 level are reported along with data for model fit. The authors are aware that the use of stepwise regression methods has several limitations and that there are alternatives to this carboxypeptidase a approach (e.g., testing the final model in an independent sample), but given the peculiarity of the study sample, that is, the limited availability of European medical assessors, the uniqueness of the sample population, and the number of variables included for testing (DOSPERT, Big Five taxonomy), the chosen approach appeared to be the most pragmatic. All statistical analyses were conducted using SPSS 18.

Results

Demographic Characteristics

Of the 80 assessors enrolled in the study, 75 (94%) responded in phase 1, while 59 (73%) assessors completed phases 2 and 3. No difference was found for age, sex, role in the agency, regulatory experience, or therapeutic area expertise between dropouts from phase 1 and those who continued on to phases 2 and 3.

As presented in Table 1, the group was equally balanced by sex; 31% were between 20 and 39 years old. Many assessors had multiple educational degrees; 51% of the assessors were medically qualified, followed by PhD (29%) and pharmacists (13%). Assessors within the NCAs generally focus on single area of expertise. In our sample, most of the assessors were experts in assessing clinical efficacy (63.8%). Assessors with less than 5 years of experience comprised most of the group (55%).

Table 1.
Demographic characteristics.VariableCharacteristicFrequencySexMale38Female37Age (y)Between 20 and 291Between 30 and 3922Between 40 and 4930Between 50 and 5918>603Professional qualificationsMD27MD/PhD11PhD19PhD/Pharm3Pharmacist10Other5Role in NCACHMP member6Internal assessor57External assessor9Other3Years of regulatory experience by country<5 y5+ yFrance28Spain43The Netherlands83United Kingdom46Germany37Austria91Italy100Ireland03Portugal13CHMP, Committee for Medicinal Purposes for Human Use; NCA, National Competent Authority.Full-size tableTable optionsView in workspaceDownload as CSV

Risk Attitudes among Assessors

The mean scores for the DOSPERT scales (risk taking and risk perceptions) for the five domains (social, financial, health/safety, recreational, and ethical) are trichocysts given in Table 2. When the domain subscale scores for both risk taking and risk perception scales were categorized by domain, assessors were predominantly risk neutral/tolerant, with the remaining assessors evenly distributed among the other categories (Table 3). When the risk taking scale was evaluated across the domains as presented in Table 4, 2.5% of the assessors were risk seeking for all domains, no assessor was risk averse for all domains, and 15% of the assessors were neutral/tolerant in their GRA. Similarly, for the risk perception scale, 2.5% of the assessors were categorized as being “perceived risk seeking” for all domains and 2.5% were “perceived risk averse” for all domains, while 17.5% of the assessors were perceived risk neutral/tolerant.

Table 4.
DOSPERT scale—Risk attitudes across all domains.Risk attitude categoriesGeneral risk attitude (from the Risk Taking subscale)Perceived risk attitude (from the Risk Perception subscale)N = 75%N = 75%Seeking22.522.5Seeking neutral2632.52835.0Neutral1215.01417.5Neutral averse2430.02531.2Averse0022.5Mixed1113.845.0DOSPERT, Domain Specific Risk Taking.Full-size tableTable optionsView in workspaceDownload as CSV

br The results of measurements of bulk hardness HV of

The results of measurements of bulk hardness (HV 10) of differently heat treated specimens are shown in Fig. 9. The as-quenched hardness for conventionally heat treated steel austenitized at 1000, 1025, 1050 and 1075 °C was 871 ± 7, 870 ± 5, 875 ± 16 and 866 ± 13 HV 10, respectively. Corresponding hardness values of the steel, SZT at a temperature of −196 °C were recorded to be 897 ± 9, 912 ± 26, 941 ± 8 and 925 ± 27 HV 10, respectively. These results infer that the as-quenched bulk hardness of the Vanadis 6 steel is improved due to the sub-zero treatment, over whole range of austenitizing temperatures used for the investigations.

Fig. 9. Bulk hardness HV 10 of the material as a function of austenitizing temperature and parameters ruthenium red of SZT.Figure optionsDownload full-size imageDownload high-quality image (144 K)Download as PowerPoint slide

The improvement in as-quenched bulk hardness of SZT steel over the CHT one can be directly related to the extent of reduction of soft retained austenite and increased ruthenium red density of small globular carbides (SGCs) in SZT steel. Similar hardness results achieved by sub-zero treatment were reported earlier for AISI D2 steel [5]; [6]; [10]; [19]; [29]; [30] ;  [31] and AISI D3 steel [32], respectively. It seems to be natural that the variations in bulk hardness should be directly related to the characteristics of matrix and carbides. Das et al. [4] have shown that microhardness (HV 0.05) of the matrix of AISI D2 steel increases by several percents due to sub-zero treatment. Hence, the contribution of elevated matrix microhardness to improved bulk hardness of sub-zero treated steel can be considered as natural. Positive contribution of SGCs can be related to their higher population density and reduction of interparticle spacing, which is also in line with previous investigations [4] ;  [21].

4. Conclusions

The obtained experimental results lead to the following major conclusions:i)As-annealed Vanadis 6 steel contains ferritic matrix and two types of carbides, namely the eutectic MC-phase and the M7C3-paticles, which are of both the secondary and the pearlitic origin. After the heat treatment, the steel is composed of the martensite, retained austenite and undissolved carbides.ii)Sub-zero treatment results in more complete martensitic transformation. The higher austenitizing temperature the more significant is the reduction of the amount of retained austenite.iii)Martensite formed during sub-zero treatment is refined compared to that produced by conventional heat treatment, in terms of size of martensitic domains. Moreover, it manifests higher dislocation density and contains carbon-rich and carbon-depleted areas.iv)Sub-zero treatment of the Vanadis 6 steel results in acceleration of the decomposition of martensite, which is demonstrated mainly by enhanced amount of small globular carbides. These carbides have a size of around 100 nm in most cases. The nature of small globular carbides is the orthorhombic cementite. Characterization of these particles reveals that sub-zero treatment does not alter the nature of these carbides but only their amount.v)As-quenched hardness of the Vanadis 6 steel manifests a moderate increase due to the sub-zero treatment. The increase in as-quenched hardness is more significant when higher austenitizing temperature is used for the treatment, which is associated with more pronounced reduction of the retained austenite amount and higher volume fraction of small globular carbides.

AcknowledgementsThis paper is a result of preliminary experiments of the project VEGA 1/0735/14.

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